Subjects -> MANUFACTURING AND TECHNOLOGY (Total: 362 journals)
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    - PLASTICS (42 journals)
    - RUBBER (4 journals)

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Techniques et culture     Open Access   (Followers: 1)
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Underwater Technology: The International Journal of the Society for Underwater     Full-text available via subscription   (Followers: 1)
World Review of Science, Technology and Sustainable Development     Hybrid Journal   (Followers: 4)
Вісник Приазовського Державного Технічного Університету. Серія: Технічні науки     Open Access  

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Structural Health Monitoring
Journal Prestige (SJR): 0.849
Citation Impact (citeScore): 3
Number of Followers: 6  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1475-9217 - ISSN (Online) 1741-3168
Published by Sage Publications Homepage  [1169 journals]
  • Damage detection in cementitious materials with optimized absolute
           electrical resistance tomography

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      Authors: Xiaoyong Zhou, Jiahui Wang, Fubin Tu, Prakash Bhat
      Abstract: Structural Health Monitoring, Ahead of Print.
      Electrical resistance tomography (ERT) serves as a non-invasive, non-destructive, non-radioactive imaging technique. It has potential applications in industrial and biological imaging. This paper presents an optimized inverse algorithm, named Newton’s Constrained Reconstruction Method (NCRM), to detect damage in cementitious materials. Several constraints were utilized in the proposed algorithm to optimize initial parameters. The range and spatial distribution of conductivities within the sample were chosen as two main constraints. Two sets of numerical and a set of experimental voltage data were used to reconstruct conductivity distribution images based on this algorithm. To evaluate the quality of reconstructed images, two image quality evaluation indicators, correlation coefficient and position error were used. Results show that the proposed algorithm NCRM has the ability to enhance the reconstructed image quality with fewer artifacts and has better positioning accuracy.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-04T08:16:01Z
      DOI: 10.1177/14759217211059066
       
  • Sensor data-based probabilistic monitoring of time-history deflections of
           railway bridges induced by high-speed trains

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      Authors: Jaebeom Lee, Seunghoo Jeong, Junhwa Lee, Sung-Han Sim, Kyoung-Chan Lee, Young-Joo Lee
      Abstract: Structural Health Monitoring, Ahead of Print.
      Structural condition monitoring of railway bridges has been emphasized for guaranteeing the passenger comfort and safety. Various attempts have been made to monitor structural conditions, but many of them have focused on monitoring dynamic characteristics in frequency domain representation which requires additional data transformation. Occurrence of abnormal structural responses, however, can be intuitively detected by directly monitoring the time-history responses, and it may give information including the time to occur the abnormal responses and the magnitude of the dynamic amplification. Therefore, this study suggests a new Bayesian method for directly monitoring the time-history deflections induced by high-speed trains. To train the monitoring model, the data preprocessing of speed estimation and data synchronization are conducted first for the given training data of the raw time-history deflection; the Bayesian inference is then introduced for the derivation of the probability-based dynamic thresholds for each train type. After constructing the model, the detection of the abnormal deflection data is proceeded. The speed estimation and data synchronization are conducted again for the test data, and the anomaly score and ratio are estimated based on the probabilistic monitoring model. A warning is generated if the anomaly ratio is at an unacceptable level; otherwise, the deflection is considered as a normal condition. A high-speed railway bridge in operation is chosen for the verification of the proposed method, in which a probabilistic monitoring model is constructed from displacement time-histories during train passage. It is shown that the model can specify an anomaly of a train-track-bridge system.
      Citation: Structural Health Monitoring
      PubDate: 2022-01-03T12:06:00Z
      DOI: 10.1177/14759217211063424
       
  • Experimental investigation of fiber Bragg grating hoop strain
           sensor–based method for sudden leakage monitoring of gas pipeline

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      Authors: Tao Jiang, Liang Ren, Jia-jian Wang, Zi-guang Jia, Dong-sheng Li, Hong-nan Li
      Pages: 3024 - 3035
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3024-3035, November 2021.
      Pipeline, serving as one of the most important gas transportation methods, can cause serious consequences in the event of leakage, so leakage monitoring is particularly important. In our previous study, a fiber Bragg grating hoop strain sensor was developed to detect pipeline leakage by measuring circumferential strain, and this sensor was utilized in a liquid pipeline leakage test to verify its performance. In this article, we established a full-scale gas pipeline model and investigated the performance of this fiber Bragg grating hoop strain sensor in leakage monitoring. On the basis of circumferential strain variation characteristic before and after negative pressure wave arrival at a fiber Bragg grating hoop strain sensor, a linear fitting algorithm combined with threshold detection was proposed to capture the knee point of circumferential strain. Test results illustrated that the proposed approach has good performance in knee point detection, and fiber Bragg grating hoop strain sensor is suitable for gas pipeline leakage monitoring.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-01T07:19:02Z
      DOI: 10.1177/1475921720978619
      Issue No: Vol. 20, No. 6 (2021)
       
  • Crack damage detection of structures using spectral transfer matrix

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      Authors: P. Nandakumar, K. Shankar
      Pages: 3036 - 3055
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3036-3055, November 2021.
      A novel spectral transfer matrix for a cracked beam element is developed in this article and the same is used to identify the crack parameters on the beam structures. Spectral transfer matrix is developed from trigonometric functions based on the theory of fracture mechanics. This matrix determines the natural frequencies of a structure with crack with better accuracy than any other transfer matrices in the literature. The state vector at a node on the structure is formed which includes the displacement, rotation, internal and external forces, and moments at that node. When the state vector is multiplied with the transfer matrix, the state vector at the adjacent node is obtained. Each element is assumed to have a single open breathing crack with unknown depth and location. Initially, the developed spectral transfer matrix is used to determine the natural frequencies of a known cantilever, and after successful validation, the same is used for crack damage detection. By an inverse approach, crack parameters in each element are identified. The state vector at one node on the structure is obtained by measurement of input and out responses which is known as the initial state vector. Acceleration responses at selected nodes on the structure are measured and the state vectors at those nodes are predicted using spectral transfer matrices. The mean square error between measured and simulated responses is minimized using a heuristic optimization algorithm, with crack depth and location in each element as the optimization variables. Spectral transfer matrix method is applied to two numerical problems with single crack in each element; later, this method is successfully validated experimentally with structures having different boundary conditions. The accuracy in identified crack parameters and the applicability to sub-structures of a large structure are the important aspects of this method.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-01T07:19:00Z
      DOI: 10.1177/1475921720979286
      Issue No: Vol. 20, No. 6 (2021)
       
  • A novel time–frequency transform for broadband Lamb waves dispersion
           characteristics analysis

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      Authors: Zhi Luo, Liang Zeng, Jing Lin
      Pages: 3056 - 3074
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3056-3074, November 2021.
      Owing to carrying rich information about structure flaws, broadband Lamb waves are considered as a promising tool for non-destructive testing. However, since every Lamb wave mode has its own dispersion characteristics, the feature extraction among broadband multimodal Lamb wave is challenging. Time–frequency representation is significantly effective to analyze dispersive signals. In this article, taking advantages of the idea of dispersion compensation, two kinds of time–frequency domain dispersion analysis methods for broadband Lamb wave were proposed. The first one is based on the concept of the general parameterized time–frequency transform. A kernel function related to group delay was designed and the time–frequency compensation transform was proposed. The other one combines the segment linear mapping technique and the short-frequency Fourier transform, called the time–frequency de-dispersion transform. Both these two methods work well in representing multimodal Lamb wave signals with high resolution. However, time–frequency de-dispersion transform outperforms in representing multipath Lamb waves than time–frequency compensation transform. Moreover, a mode purification strategy was also proposed for distinguishing the interested mode from interferences. According to verification in synthetic and experimental data, not only the multimodal components but also multipath echoes are represented in time–frequency plane with high resolution. Finally, the proposed method shows a great robustness to inaccuracies in the dispersion data.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-15T04:57:18Z
      DOI: 10.1177/1475921720979283
      Issue No: Vol. 20, No. 6 (2021)
       
  • Research on early weak structural damage detection of aeroengine
           intershaft bearing based on acoustic emission technology

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      Authors: Zheming Liang, Anna Wang, Yang Yu, Ping Yang
      Pages: 3113 - 3122
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3113-3122, November 2021.
      Until now, there is no effective method to detect the early weak structural damage of the aero engine intershaft bearing. In this article, an online method based on acoustic emission technology is used to detect the early weak structural damage of aeroengine intershaft bearing. The combination of maximum correlated kurtosis deconvolution and bandpass filter is proposed to enhance and denoise fault characteristic signals of the aero engine intershaft bearing. The Hilbert envelope demodulation is used to extracting the bear fault characteristic frequency. Under aeroengine working conditions, the experiment of an intershaft bearing with outer ring fault is carried out. The result shows that bearing outer ring fault characteristic frequency and bearing vibration frequencies clearly appear in the envelope spectrum of the acoustic emission signal. Therefore, online detection of early weak structure damage of aeroengine intershaft bearing is realized by acoustic emission technology under the background of strong noise.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-01T07:19:08Z
      DOI: 10.1177/1475921720980356
      Issue No: Vol. 20, No. 6 (2021)
       
  • Using a single sensor for bridge condition monitoring via moving embedded
           principal component analysis

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      Authors: Zhenhua Nie, Zhaofeng Shen, Jun Li, Hong Hao, Yizhou Lin, Hongwei Ma, Hui Jiang
      Pages: 3123 - 3149
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3123-3149, November 2021.
      This article presents a novel data-driven structural damage detection method named moving embedded principal component analysis to monitor the bridge condition and detect the damage occurrence using only one sensor. A fixed moving window is used to cut out the time series of the recorded data for the analysis. The data set inside the window is embedded to be a multidimensional state space using time delay method. The matrix of the state space is analyzed using the standard principal component analysis method, and a novel damage index Rj defined with the eigenvalue is proposed to identify structural damage occurrence. The window length is determined by a new approach through examining the convergent spectrum of the contribution ratio of the first principal component of the embedded state space. The time delay is determined by the autocorrelation function of the response, and the embedding dimension is obtained by the cumulative contribution ratio of the state space. The windowed damage index can be calculated continuously by moving the window along the recorded vibration data. To demonstrate the performance of the proposed method, responses of a beam bridge model subjected to stochastic loads obtained with numerical simulations and experimental tests are analyzed to monitor the structural conditions. The results demonstrate that the proposed method can accurately identify the occurrence of damage and the abnormal behavior of the structure. The recorded data on a large suspension bridge are also analyzed. The analysis successfully identified an incident on this bridge when it was slightly scraped by the mast of a sand ship. This further verifies the effectiveness of the proposed method.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-01T07:19:15Z
      DOI: 10.1177/1475921720980516
      Issue No: Vol. 20, No. 6 (2021)
       
  • A dynamic harmonic regression approach for bridge structural health
           monitoring

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      Authors: Tadhg Buckley, Vikram Pakrashi, Bidisha Ghosh
      Pages: 3150 - 3181
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3150-3181, November 2021.
      Structural damage in a bridge is defined as a significant deviation in the structural response from its standard operating conditions, not explainable by variations in external environmental and operational effects. However, environmental effects such as temperature fluctuations can cause significant seasonal variations in the structural response of a bridge and can mask its changes due to structural damage. This poses a challenge for structural health monitoring of bridges where reliable diagnosis of damage or deterioration is often related to isolation of the responses. To address it, a statistical damage-detection methodology is introduced where strain data are modelled using a dynamic harmonic regression time-series model. Prediction intervals of multi-step ahead forecasts from the dynamic harmonic regression model are then used as statistical control limits within which the observed phenomenon should fall under standard operating conditions. This single recursive structural health monitoring framework for automatic fitting and multi-step ahead forecasting of 1-min interval time-series strain data includes recorded temperature values and diurnal trends as regressors in the model to account for environmental variations. The potential of this method as a robust automatic structural health monitoring strategy is demonstrated on strain data sampled at 1-min interval from a full-scale damaged pre-stressed concrete bridge – before, during and after repair. The proposed method can capture both sudden and daily changes in structural response due to temperature effects, and a rolling multi-step ahead interval forecast was able to identify damage on back-cast data transitioning from a healthy state to a damaged state.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-25T05:25:13Z
      DOI: 10.1177/1475921720981735
      Issue No: Vol. 20, No. 6 (2021)
       
  • Fault parameter identification in rotating system: Comparison between
           deterministic and stochastic approaches

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      Authors: Gabriel Yuji Garoli, Diogo Stuani Alves, Tiago Henrique Machado, Katia Lucchesi Cavalca, Helio Fiori de Castro
      Pages: 3182 - 3200
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3182-3200, November 2021.
      Fault identification is a recurrent topic in rotating machines field. The evaluation of fault parameters allows better maintenance of such expensive and, sometimes, large machines. Unbalance is one of the most common faults, and it is inherent to rotors functioning. Wear in journal bearings is another common fault, caused by several start/stop cycles – when at low rotating speed, there is still contact between shaft and bearing wall. Fault parameter identification generally uses deterministic model–based methods. However, these methods do not take into account the uncertainties inherently involved in the identification process. The stochastic approach by the Bayesian inference is, then, used to account the uncertainties of the fault parameters. The generalized polynomial chaos expansion is proposed to evaluate the inference, due to its faster performance regarding the Markov chain Monte Carlo methods. Deterministic and stochastic approaches were compared; all were based on experimental vibration measurements of the shaft inside the journal bearings. The Bayesian inference with the polynomial chaos showed reliable and promising results for identification of unbalance and bearing wear fault parameters.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-25T05:27:14Z
      DOI: 10.1177/1475921720981737
      Issue No: Vol. 20, No. 6 (2021)
       
  • Change detection using the generalized likelihood ratio method to improve
           the sensitivity of guided wave structural health monitoring systems

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      Authors: Stefano Mariani, Peter Cawley
      Pages: 3201 - 3226
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3201-3226, November 2021.
      The transition from one-off ultrasound–based non-destructive testing systems to permanently installed monitoring techniques has the potential to significantly improve the defect detection sensitivity, since frequent measurements can be obtained and tracked with time. However, the measurements must be compensated for changing environmental and operational conditions, such as temperature, and careful analysis of measurements by highly skilled operators quickly becomes unfeasible as a large number of sensors acquiring signals frequently is installed on a plant. Recently, the authors have developed a location-specific temperature compensation method that uses a set of baseline measurements to remove temperature effects from the signals, thus producing “residual” signals on an unchanged structure that are essentially normally distributed with zero-mean and with standard deviation related to instrumentation noise. This enables the application of change detection techniques such as the generalized likelihood ratio method that can process sequences of residual signals searching for changes caused by damage. The defect detection performance offered by the generalized likelihood ratio when applied to guided wave signals adjusted either via the newly developed location-specific temperature compensation method or the widely used optimal baseline selection technique is investigated on a set of simulated measurements based on a set of experimental signals acquired by a permanently installed pipe monitoring system designed to monitor tens of meters of pipe from one location using the torsional, T(0,1), guided wave mode. The results presented here indicate that damage on the order of 0.1% cross section loss can reliably be detected with virtually zero false calls if the assumptions of the study are met, notably the absence of sensor drift with time. This represents a factor of 20–50 improvement over that typically achieved in one-off inspection and makes such monitoring systems very attractive. The method will also be applicable to bulk wave ultrasound signals.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-01T07:18:56Z
      DOI: 10.1177/1475921720981831
      Issue No: Vol. 20, No. 6 (2021)
       
  • Parameter identification of crack-like notches in aluminum plates based on
           strain gauge data

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      Authors: Ramdane Boukellif, Andreas Ricoeur, Matthias Oxe
      Pages: 3227 - 3238
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3227-3238, November 2021.
      The identification of crack parameters and stress intensity factors in aluminum plates under tensile loading is in the focus of the presented research. In this regard, data of strain gauges, distributed along the edges of the samples, are interpreted. In the experiments, slit-shaped notches take the role of cracks located in the interior of the specimens. Their positions, inclinations and lengths as well as the magnitudes of external loadings are identified solving the inverse problems of cracked plates and associated strain fields. Exploiting the powerful approach of distributed dislocations, based on Green’s functions provided by the framework of linear elasticity, in conjunction with a genetic algorithm, allows for a very efficient identification of the sought parameters, thus being suitable for in situ monitoring of engineering structures. Tested samples exhibit one or two straight crack-like notches as well as a kinked one.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-01T07:19:10Z
      DOI: 10.1177/1475921720981845
      Issue No: Vol. 20, No. 6 (2021)
       
  • Improved damage detection in Pelton turbines using optimized condition
           indicators and data-driven techniques

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      Authors: Weiqiang Zhao, Mònica Egusquiza, Aida Estevez, Alexandre Presas, Carme Valero, David Valentín, Eduard Egusquiza
      Pages: 3239 - 3251
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3239-3251, November 2021.
      The health condition of hydraulic turbines is one of the most critical factors for the operation safety and financial benefits of a hydro power plant. After the massive entrance of intermittent renewable energies, hydropower units have to regulate their output much more frequently for the balancing of the power grid. Under these conditions, the components of the machine have to withstand harsher excitation forces, which are more likely to produce damage and eventual failure in the turbines. To ensure the reliability of these machines, improved condition monitoring techniques are increasingly demanded.In this article, the feasibility of upgrading condition monitoring of Pelton turbines using novel vibration indicators and data-driven techniques is discussed. The new indicators are selected after performing a detailed analysis of the dynamic behavior of the turbine using numerical models and field measurements. After that, factor analysis is carried out in order to assess which are the most informative indicators and to reduce the dimension of the input data.For the validation of the proposed method, monitoring data from an actual Pelton turbine that suffered from an important fatigue failure due to a crack propagation on the buckets have been used. The novel condition indicators as well as classical indicators based on the spectrum and harmonics levels have been obtained while the machine was in good operation, during different stages of damage and after repair. All of these have been used to train an artificial neural network model in order to predict the evolution of the crack until failure occurs. The results show that using the improved monitoring methodology enhances the ability to predict the appearance of damage in comparison to typical condition indicators.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-08T07:42:23Z
      DOI: 10.1177/1475921720981839
      Issue No: Vol. 20, No. 6 (2021)
       
  • Nonlinear modulation with low-power sensor networks using undersampling

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      Authors: Peter Oppermann, Lennart Dorendorf, Marcus Rutner, Christian Renner
      Pages: 3252 - 3264
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3252-3264, November 2021.
      Nonlinear modulation is a promising technique for ultrasonic non-destructive damage identification. A wireless sensor network is ideally suited to monitor large structures using nonlinear modulation in a cost-efficient manner. However, existing approaches rely on high sampling rates and resource-demanding computations that are not feasible on low-cost and low-power sensor network devices. We present a new damage indicator that uses the short-time Fourier transform to derive amplitude and phase modulation with less computational effort and memory usage. Evaluation of the proposed method using real experiment data exhibits performance and reliability similar to the conventionally used modulation index. Undersampling is demonstrated, which reduces the memory demand in a test scenario by more than 100 times, and the required energy for sampling and processing more than four times. The loss of accuracy introduced by undersampling is shown to be negligible.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-15T04:57:16Z
      DOI: 10.1177/1475921720982885
      Issue No: Vol. 20, No. 6 (2021)
       
  • The use of satellite data to support the structural health monitoring in
           areas affected by slow-moving landslides: a potential application to
           reinforced concrete buildings

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      Authors: Andrea Miano, Annalisa Mele, Domenico Calcaterra, Diego Di Martire, Donato Infante, Andrea Prota, Massimo Ramondini
      Pages: 3265 - 3287
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3265-3287, November 2021.
      The building stock around the world is exposed to different types of natural actions such as earthquakes or landslides. In particular, Italy is one of the countries worldwide most affected by landslides. Mitigation of landslide risk is a topic of great interest for the evaluation and management of its consequences. Periodical monitoring of the landslide-induced damage on structures require high costs due to the large number of exposed elements. With respect to the reinforced concrete structures, slow-moving landslides can affect primary structural elements, but more frequently damage occurs on the most vulnerable elements of the structure such as infills. The aim of this work is to demonstrate the potential utility of satellite data derived from a remote sensing technique, known as differential synthetic aperture radar interferometry, to support the structural health monitoring of reinforced concrete buildings affected by landslides. This article shows the structural health monitoring process for a reinforced concrete infilled building within a landslide-affected area, using the differential synthetic aperture radar interferometry data as input for the structural analysis in order to investigate the evolution of damage over the years. Three-dimensional structure, including the explicit infills consideration, has been modeled based on the information available from a visual survey, obtaining the missing parameters from a simulated design process and from the literature. In the field of the civil protection programs for the landslide risk reduction, this methodology can be quickly repeated for large sets of reinforced concrete buildings. Evidence of the visual survey showed a significant damage pattern in some infills. A good agreement has been found between analytical previsions and existing damage. Moreover, a global infills damage assessment of the case study building is proposed. Finally, assuming a constant increase in displacements in future years, a prediction of the future expected damage is shown.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-25T05:29:34Z
      DOI: 10.1177/1475921720983232
      Issue No: Vol. 20, No. 6 (2021)
       
  • Influence of thermal expansion bend and tubesheet geometry on guided wave
           inspection of steam generator tubes of a fast breeder reactor

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      Authors: MM Narayanan, V Arjun, Anish Kumar
      Pages: 3288 - 3308
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3288-3308, November 2021.
      Periodic assessment of steam generator tubes of a sodium-cooled nuclear reactor is very crucial for smooth operation of steam generators. To examine the integrity, an in-bore magnetostrictive transducer capable of launching and receiving longitudinal ultrasonic guided waves (L(0,2) mode) from the inner diameter side of a steam generator tube developed in-house is used. Preliminary tests conducted on defective steam generator tubes with thermal expansion bends (three successive bends) of the mockup steam generator test facility yield a good sensitivity of 20% wall thickness deep flaw (0.46-mm deep and 1-mm wide half-circumferential groove) and the location accuracy of 10 mm. In order to remove high noise, wavelet-based denoising using discrete wavelet transform is used which improves the signal-to-noise ratio by 5–10 dB. In addition, cross-correlation technique is also used to denoise and unambiguously identify the defect echoes amid noise and multiple reflections between the defects. Furthermore, influence of the thermal expansion bend and tubesheet–spigot structure on L(0,2) mode is studied using the finite element analysis. It is observed that in the thermal expansion (multiple) bend, axisymmetric L(0,2) mode becomes non-axisymmetric (maximum and minimum amplitudes at extrados and intrados, respectively) and undergoes mode conversion to a weak flexural mode F(1,3). The results are validated experimentally. In the tubesheet–spigot structure, L(0,2) mode is found to have ∼10% reflection from spigot–tubesheet transitions, and it is seen to mode convert to bulk waves in the tubesheet. In conclusion, thicker tubesheets are found to be better from the perspective of inspection.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-25T05:32:14Z
      DOI: 10.1177/1475921720983520
      Issue No: Vol. 20, No. 6 (2021)
       
  • Assessment and visualization of performance indicators of reinforced
           concrete beams by distributed optical fibre sensing

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      Authors: Carlos G Berrocal, Ignasi Fernandez, Mattia Francesco Bado, Joan R Casas, Rasmus Rempling
      Pages: 3309 - 3326
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3309-3326, November 2021.
      The implementation of structural health monitoring systems in civil engineering structures already in the construction phase could contribute to safer and more resilient infrastructure. Due to their lightweight, small size and high resistance to the environment, distributed optical fibre sensors stand out as a very promising technology for damage detection and quantification in reinforced concrete structures. In this article, the suitability of embedding robust distributed optical fibre sensors featuring a protective sheath to accurately assess the performance indicators, in terms of vertical deflection and crack width, of three reinforced concrete beams subjected to four-point bending is investigated. The results revealed that a certain strain attenuation occurs in embedded robust distributed optical fibre sensors compared to commonly used thin polyimide-coated distributed optical fibre sensors bonded to steel reinforcement bars. However, the presence of the protective sheath prevented the appearance of strain reading anomalies which has been a frequently reported issue. Performance wise, the robust distributed optical fibre sensors were able to provide a good estimate of the beam deflections with errors of between 12.3% and 6.5%. Similarly, crack widths computed based on distributed optical fibre sensor strain measurements differed by as little as ±20 µm with results from digital image correlation, provided individual cracks could be successfully detected in the strain profiles. Finally, a post-processing procedure is presented to generate intuitive contour plots that can help delivering critical information about the element’s structural condition in a clear and straightforward manner.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-18T09:58:05Z
      DOI: 10.1177/1475921720984431
      Issue No: Vol. 20, No. 6 (2021)
       
  • Laboratory investigation of a bridge scour monitoring method using
           decentralized modal analysis

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      Authors: Muhammad Arslan Khan, Daniel P McCrum, Luke J Prendergast, Eugene J OBrien, Paul C Fitzgerald, Chul-Woo Kim
      Pages: 3327 - 3341
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3327-3341, November 2021.
      Scour is a significant issue for bridges worldwide that influences the global stiffness of bridge structures and hence alters the dynamic behaviour of these systems. For the first time, this article presents a new approach to detect bridge scour at shallow pad foundations, using a decentralized modal analysis approach through re-deployable accelerometers to extract modal information. A numerical model of a bridge with four simply supported spans on piers is created to test the approach. Scour is modelled as a reduction in foundation stiffness under a given pier. A passing half-car vehicle model is simulated to excite the bridge in phases of measurement to obtain segments of the mode shape using output-only modal analysis. Two points of the bridge are used to obtain modal amplitudes in each phase, which are combined to estimate the global mode shape. A damage indicator is postulated based on fitting curves to the mode shapes, using maximum likelihood, which can locate scour damage. The root mean square difference between the healthy and scoured mode shape curves exhibits an almost linear increase with increasing foundation stiffness loss under scour. Experimental tests have been carried out on a scaled model bridge to validate the approach presented in this article.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-15T04:57:11Z
      DOI: 10.1177/1475921720985122
      Issue No: Vol. 20, No. 6 (2021)
       
  • Effect of propagation distance on acoustic emission of carbon fiber/epoxy
           composites

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      Authors: Doyun Jung, Byoung-Sun Lee, Woong-Ryeol Yu, Wonjin Na
      Pages: 3342 - 3353
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3342-3353, November 2021.
      This study examined the change in acoustic emission as a function of measurement position of fiber-reinforced composites. Single-edge-notched carbon fiber/epoxy composites were prepared and tested under cyclic loading, with sensors located at specific distances from the end of the notch. Although the Ib-value increased overall, the degree of increase significantly varied with position and acoustic emission frequency. Notably, the proportion of acoustic emission signals for each failure mode varied due to a high attenuation rate at high frequencies, which increased the Ib-value. Accordingly, the high-frequency fiber-failure signals significantly affected the Ib-value. This study focused on the importance of analyzing acoustic emission signals by considering the crack location and frequency-dependent attenuation rate. We concluded that an acoustic emission sensor should be located 20–40 mm from the crack location for woven carbon fiber/epoxy composites.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-15T04:57:12Z
      DOI: 10.1177/1475921720986156
      Issue No: Vol. 20, No. 6 (2021)
       
  • A new fault diagnosis method based on adaptive spectrum mode extraction

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      Authors: Zhijian Wang, Ningning Yang, Naipeng Li, Wenhua Du, Junyuan Wang
      Pages: 3354 - 3370
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3354-3370, November 2021.
      Variational mode decomposition provides a feasible method for non-stationary signal analysis, but the method is still not adaptive, which greatly limits the wide application of the method. Therefore, an adaptive spectrum mode extraction method is proposed in this article. The proposed method is mainly composed of spectral segmentation, mode extraction, and feedback adjustment. In the spectral segmentation part, considering the lack of robustness of classical scale space in a strong noise environment, this article proposes a method of fault feature mapping, which solves over-decomposition of variational mode decomposition guided by classical scale space. In the mode extraction part, for insufficient self-adaptability of the single penalty factor in the variational mode decomposition method, this article proposes a method of spectral aggregation factor, which solves multiple penalty factors that conform to different intrinsic modal functions. In the feedback adjustment part, this article proposes the method of transboundary criterion, which makes the result of variational mode decomposition within a preset range. Finally, using dispersion entropy and kurtosis indicators, intrinsic modal function components containing fault frequencies are extracted for envelope spectrum analysis to extract fault characteristic frequencies. In order to verify the effectiveness of the proposed method, the proposed method is applied to simulation signals and bearing fault signals. Comparing the decomposition results of different methods, the conclusion shows that the proposed method is more advantageous for the fault feature extraction of rolling bearings.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-21T07:35:40Z
      DOI: 10.1177/1475921720986945
      Issue No: Vol. 20, No. 6 (2021)
       
  • An integrated approach for structural behavior characterization of the
           Gravina Bridge (Matera, Southern Italy)

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      Authors: Vincenzo Serlenga, Maria Rosaria Gallipoli, Rocco Ditommaso, Carlo Felice Ponzo, Nicola Tragni, Angela Perrone, Tony Alfredo Stabile, Giuseppe Calamita, Luigi Vignola, Raffaele Franco Carso, Domenico Pietrapertosa, Vincenzo Lapenna
      Pages: 3371 - 3391
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3371-3391, November 2021.
      An integrated geophysical approach using non-invasive, non-destructive, and cost-effective seismic and electromagnetic techniques has been implemented to recognize the static and dynamic properties (i.e. eigenfrequencies, equivalent viscous damping factors, and related modal shapes) of the Gravina Bridge and its interaction with foundation soils. The “Gravina” is a bow-string bridge located on outcropping calcarenites in the city of Matera (Southern Italy) and develops for 144 m along a steel-concrete deck. The foundation soil characteristics have been evaluated by means of three high-resolution geo-electrical tomographies, one Vs velocity profile, and two site amplification functions. The main structural characteristics of the bridge have been estimated through permanent and on-demand monitoring using seismic and electromagnetic sensing. The former consisted of accelerometers and velocimeters installed with different geometrical arrangements for permanent earthquake and on-demand ambient vibration test recordings. The electromagnetic sensing was realized by a microwave radar interferometer placed below the deck to measure the displacements of the whole scenario illuminated by the antenna beam providing a continuous mapping of the static and dynamic displacements of the entire target. Acquired data have been analyzed in both frequency and time-frequency domain with the aim to study the stationary and non-stationary response of the monitored bridge. These experimental campaigns allowed us to assess the robustness of the proposed approach and to set up the zero-time reference point of the bridge dynamic parameters.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-25T07:10:00Z
      DOI: 10.1177/1475921720987544
      Issue No: Vol. 20, No. 6 (2021)
       
  • Influence of cable tension history on the monitoring of cable tension
           using magnetoelastic inductance method

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      Authors: Senhua Zhang, Jianting Zhou, Hong Zhang, Leng Liao, Lei Liu
      Pages: 3392 - 3405
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3392-3405, November 2021.
      Cable tension monitoring is vital for the health monitoring of cable-stayed bridges. During the service of bridges, cable tension fluctuates rather than monotonously changes. However, existing research works pay little attention to the influence of tension history. In this article, the influence of the tension history on the monitoring of cable tension was studied. To guide the experiment, the magnetization theory of ferromagnetic materials and the electromagnetic induction principle were combined to analyze the theory of the magnetoelastic inductance method. The magnetoelastic inductance method characterized cable tension by sensor inductance. Based on the theoretical analysis, tension monitoring experiments were carried out to figure out the influences of design tension and tension variation. Experimental results showed the design tension and the tension variation influenced the relationship between the inductance and the tension. To monitor the fluctuating tension, a secant method was proposed. When the tension changed less than 30% of the design tension, the tension can be ascertained by the secant method. The experimental results demonstrated that the influence of the tension history should be considered when the design tension was different or the tension variation was large. Besides, the influence of the tension history analyzed in this article is suitable for other tension monitoring methods based on the magnetic properties of ferromagnetic materials.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-25T07:07:43Z
      DOI: 10.1177/1475921720987987
      Issue No: Vol. 20, No. 6 (2021)
       
  • Development of an integrated sacrificial sensor for damage detection and
           monitoring in composite materials and adhesively bonded joints

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      Authors: G Ólafsson, RC Tighe, SW Boyd, JM Dulieu-Barton
      Pages: 3406 - 3423
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3406-3423, November 2021.
      Quality assurance of adhesively bonded joints is of vital importance if their benefits are to be exploited across a wide range of industrial applications. A novel lightweight, low-cost, non-invasive embedded sacrificial sensor is proposed, capable of detecting damage within an adhesively bonded joint, which could also be used in a laminated composite structure. The sensor operation uses changes in electrical resistance, increasing as the sensing material area diminishes with damage progression. Initial tests prove the sensor concept by showing that the electrical resistance of the sensor increases proportionally with material removal, mimicking the sensor operation. Thermography is used to verify the current flow through the sensor and that any localised heating caused by the sensor is minimal. Short beam interlaminar shear strength (ILSS) tests show that embedding sensors in a composite laminates did not cause a reduction in material interfacial structural performance. Finally, the in situ performance of the sensor is demonstrated in quasi-static tensile tests to failure of adhesively bonded single lap joints (SLJs) with sensors embedded in the bond line. Prior to crack initiation, an electrical response occurs that correlates with increasing applied load, suggesting scope for secondary uses of the sensor for load monitoring and cycle counting. Crack initiation is accompanied by a rapid increase in electrical resistance, providing an indication of failure ahead of crack propagation and an opportunity for timely repair. As the crack damage propagated, the electrical response of the sensor increased proportionally. The effect of the sensor on the overall structural performance was assessed by comparing the failure load of joints with and without the embedded sensor with no measurable difference in ultimate strength. The research presented in the article serves as an important first step in developing a simple yet promising new technology for structural health monitoring with numerous potential applications.
      Citation: Structural Health Monitoring
      PubDate: 2021-02-11T01:08:08Z
      DOI: 10.1177/1475921721989041
      Issue No: Vol. 20, No. 6 (2021)
       
  • Steel bridge corrosion inspection with combined vision and thermographic
           images

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      Authors: Hyung Jin Lim, Soonkyu Hwang, Hyeonjin Kim, Hoon Sohn
      Pages: 3424 - 3435
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3424-3435, November 2021.
      In this study, a faster region-based convolutional neural network is constructed and applied to the combined vision and thermographic images for automated detection and classification of surface and subsurface corrosion in steel bridges. First, a hybrid imaging system is developed for the seamless integration of vision and infrared images. Herein, a three-dimensional red/green/blue vision image is obtained with a vision camera, and a one-dimensional active infrared (IR) amplitude image is obtained from the infrared camera for temperature measurements with halogen lamps as the heat source. Subsequently, the three-dimensional red/green/blue vision image is converted to a two-dimensional chroma blue- and red-difference (CbCr) image because the CbCr image is known to be more sensitive to surface corrosion than the red/green/blue image. A combined three-dimensional (CbCr-IR) image is then constructed by fusing the two-dimensional CbCr image and the one-dimensional infrared image. For the automated corrosion detection and classification, a faster region-based convolutional neural network is constructed and trained using the combined three-dimensional CbCr-IR images of surface and subsurface corrosion on steel bridge structures. Finally, the performance of the trained, faster region-based convolutional neural network is evaluated using the images acquired from real bridges and compared with faster region-based convolutional neural networks trained by other vision and IR-based images. The uniqueness of this study is attributed to the (1) corrosion detection reliability improvements based on the fusion of vision and infrared images, (2) automated corrosion detection and classification with a faster region-based convolutional neural network, (3) detection of subsurface corrosion that is not detectable using vision images only, and (4) application to field bridge inspection.
      Citation: Structural Health Monitoring
      PubDate: 2021-02-11T01:04:46Z
      DOI: 10.1177/1475921721989407
      Issue No: Vol. 20, No. 6 (2021)
       
  • Uncertainty quantification for the distribution-to-warping function
           regression method used in distribution reconstruction of missing
           structural health monitoring data

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      Authors: Zhicheng Chen, Xinyi Lei, Yuequan Bao, Fan Deng, Yufeng Zhang, Hui Li
      Pages: 3436 - 3452
      Abstract: Structural Health Monitoring, Volume 20, Issue 6, Page 3436-3452, November 2021.
      Data loss is a common problem of structural health monitoring and adversely affects many structural health monitoring applications. Tremendous progress in missing structural health monitoring data imputation has been made in recent years, forming an important part of sensor validation. Most of the imputed data are based on estimates obtained by data-driven statistical or machine learning models; quantifying their estimation uncertainties is significant in terms of being able to perform accuracy assessments and providing more insights into the imputed data. However, this procedure has been surprisingly neglected in the structural health monitoring community. This article focuses on uncertainty quantification for the distribution-to-warping function regression method (an indirect distribution-to-distribution regression method) used in reconstructing distributions of missing data. The distribution-to-warping function regression method belongs to the framework of functional data analysis as both the predictor and response are continuous functions. The challenge of performing uncertainty quantification for the distribution-to-warping function regression method comes not only from the functional nature of warping functions but also from their inherent constraints. To this end, a functional transformation is employed to transform warping functions into a vector space, and the confidence estimation for the regression operator is conducted in the vector space based on functional principal component analysis and bootstrapping. Then, the confidence region of the conditional expectation of missing distribution (caused by data loss) can be further estimated and visualized. In addition, a calibration processing procedure is also considered to obtain improved estimates of the confidence intervals with a better coverage accuracy under the desired probability. Simulation studies are conducted to validate and illustrate the proposed method, and then, it is applied to field strain monitoring data.
      Citation: Structural Health Monitoring
      PubDate: 2021-03-02T04:54:40Z
      DOI: 10.1177/1475921721993381
      Issue No: Vol. 20, No. 6 (2021)
       
  • High resolution bolt pre-load looseness monitoring using coda wave
           interferometry

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      Authors: Dongdong Chen, Linsheng Huo, Gangbing Song
      Abstract: Structural Health Monitoring, Ahead of Print.
      This paper proposes a new concept, named the Detectable Resolution of Bolt Pre-load (DRBP), by using the coda wave interferometry (CWI) to quantitatively measure the pre-load looseness at a high resolution. Due to its characteristics of roughness, irregularity, and randomly distributed asperities, the contact surface of the bolted components can function as a natural interferometer to scatter the propagation waves. The multiply-scattered coda waves can amplify the slight changes in the travel path and show the visible perturbation in the time domain. By calculating the time-shifted correlation coefficient of coda waves before and after the slight pre-load looseness, the tiny pre-load changes can be clearly revealed. To evaluate the feasibility of the proposed method, a theoretical model considering the time shifts of coda waves and the variations of pre-load is established. Based on the acoustoelastic effect and the wave path summation theory of coda wave interferometry, the model shows that the time shifts of coda waves change linearly with the variations of pre-load. Verification experiments are conducted, and the results show that the R-square values of the fitting curves are larger than 0.9216. In addition, the proposed approach has the feature of high resolution. The ultimate pre-load resolution of the proposed approach is 0.331%, that is, when the variation of pre-load is larger than 0.331%, it can be detected. Therefore, theoretical analysis and experimental results prove that the CWI-based pre-load detection approach holds great potential for the detection of bolt pre-load looseness, especially during the initial stage.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-30T03:53:23Z
      DOI: 10.1177/14759217211063420
       
  • A baseline-free method for damage identification in pipes from local
           vibration mode pair frequencies

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      Authors: Obukho E Esu, Ying Wang, Marios K Chryssanthopoulos
      Abstract: Structural Health Monitoring, Ahead of Print.
      As structural systems approach their end of service life, integrity assessment and condition monitoring during late life becomes necessary in order to identify damage due to age-related issues such as corrosion and fatigue and hence prevent failure. In this paper, a novel method of level 3 damage identification (i.e. detection, localisation and quantification) from local vibration mode pair (LVMP) frequencies is introduced. Detection is achieved by observation of LVMP frequencies within any of the vibration modes investigated while the location of the damage is predicted based on the ranking order of the LVMP frequency ratios and the damage is quantified in terms of material volume loss from pre-established quantification relations. The proposed method which is baseline-free (in the sense that it does not require vibration-based assessment or modal data from the undamaged state of the pipe) and solely frequency-dependent was found to be more than 90% accurate in detecting, locating and quantifying damage through a numerical verification study. It was also successfully assessed using experimental modal data obtained from laboratory tests performed on an aluminium pipe with artificially inflicted corrosion-like damage underscoring a novel concept in vibration-based damage identification for pipes.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-28T09:40:01Z
      DOI: 10.1177/14759217211052335
       
  • Probability of detection, localization, and sizing: The evolution of
           reliability metrics in Structural Health Monitoring

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      Authors: Francesco Falcetelli, Nan Yue, Raffaella Di Sante, Dimitrios Zarouchas
      Abstract: Structural Health Monitoring, Ahead of Print.
      The successful implementation of Structural Health Monitoring (SHM) systems is confined to the capability of evaluating their performance, reliability, and durability. Although there are many SHM techniques capable of detecting, locating and quantifying damage in several types of structures, their certification process is still limited. Despite the effort of academia and industry in defining methodologies for the performance assessment of such systems in recent years, many challenges remain to be solved. Methodologies used in Non-Destructive Evaluation (NDE) have been taken as a starting point to develop the required metrics for SHM, such as Probability of Detection (POD) curves. However, the transposition of such methodologies to SHM is anything but straightforward because additional factors should be considered. The time dependency of the data, the larger amount of variability sources and the complexity of the structures to be monitored exacerbate/aggravate the existing challenges, suggesting that much work has still to be done in SHM. The article focuses on the current challenges and barriers preventing the development of proper reliability metrics for SHM, analyzing the main differences with respect to POD methodologies for NDE. It was found that the development of POD curves for SHM systems requires a higher level of statistical expertise and their use in the literature is still limited to few studies. Finally, the discussion extends beyond POD curves towards new metrics such as Probability of Localization (POL) and Probability of Sizing (POS) curves, reflecting the diagnosis paradigm of SHM.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-28T04:44:13Z
      DOI: 10.1177/14759217211060780
       
  • Subdomain integration method of electrical resistance tomography for
           multiple flaws detection in cementitious materials

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      Authors: Xiaoyong Zhou, Fubin Tu, Jiahui Wang, Qinggang Li
      Abstract: Structural Health Monitoring, Ahead of Print.
      Electrical Resistance Tomography (ERT) has been widely used for detecting cementitious materials with one type of flaw. To extend the ERT for multi-flaws detection in a larger concrete plate, this paper develops a subdomain integration method. The adjacent driver pattern and absolute imaging scheme of ERT are adopted to reconstruct the inner electrical conductivity field of a concrete specimen which contains three different inclusions, namely, a copper bar, a piece of plexiglass, and a drop of saline solution. The feasibility of subdomain integration method for multiple flaws detection in cementitious materials is analyzed by theoretical analyses of the equipotential line density and the image quality evaluation indicator. The concrete specimen is divided into four, nine, and 16 subdomains for detection. The image reconstruction results obtained by the subdomain detection method are compared with each other, and with the results of a global detection method. Results indicate that the effective area of subdomain largely relies on the density of equipotential lines, as well as the measurement errors. Subdomain integration method is effective in detecting a relatively large cementitious component with multi-flaws.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-27T06:08:02Z
      DOI: 10.1177/14759217211060274
       
  • Automatic quality detection system for structural objects using dynamic
           output method: Case study Vilnius bridges

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      Authors: Vytautas Bucinskas, Andrius Dzedzickis, Nikolaj Sesok, Igor Iljin, Ernestas Sutinys, Marius Sumanas, Inga Morkvenaite-Vilkonciene
      Abstract: Structural Health Monitoring, Ahead of Print.
      Paper provides an attempt to create a methodology for automated structure health monitoring procedures using vibration spectrum analysis. There is an option to use autoregressive (AR) spectral analysis to extract information from frequency spectra when conventional Fast Fourier transformation (FFT) analysis cannot give relevant information. An autoregressive spectrum analysis is widely used in optics and medicine; however, it can be applied for different purposes, such as spectra analysis in electronics or mechanical vibration. This paper presents an automated structural health monitoring approach based on the algorithm-driven definition of the first resonant frequency value from a noisy signal, acquired from traffic-created bridge vibrations. We implemented the AR procedure and developed a peak detection algorithm for experimental data processing. The functionality of the proposed methodology was evaluated by performing research on six bridges in Vilnius (Lithuania). We compared three methods of data processing: FFT, filtered FFT and AR. Bridges vibrations under different excitation conditions (wind, impulse and traffic) in normal direction were measured using accelerometers. AR provided one peak representing the lowest resonant frequency in all cases, while FFT and filtered FFT provided up to 12 peaks with similar frequency values. Such results allow implementing our method for remote automated structures health monitoring and ensure structures safety using a convenient and straightforward diagnostic method.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-24T11:11:11Z
      DOI: 10.1177/14759217211061518
       
  • Recent progress in aircraft smart skin for structural health monitoring

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      Authors: Yu Wang, Shuguang Hu, Tao Xiong, Yongan Huang, Lei Qiu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Through the integration of advanced sensors, actuators, and micro-processors, aircraft smart skin technology can improve the structural performance of aircraft and make them self-perception, self-diagnosis, self-adaptation, self-learning, and self-repair. Aircraft smart skin for structural health monitoring (SHM) is an important type of aircraft smart skin and has received extensive attention in recent years. Large-scale, lightweight, and low-power consumption are three key problems hindering the realization and engineering applications of aircraft smart skin for SHM. In view of these problems and restrictions of practical aircraft onboard applications, this article reviews the current research progress on aircraft smart skin for SHM, introduces their design, materials, manufacturing process, and monitoring principles in detail, and discusses how they study above problems from these aspects. Finally, perspectives are proposed on the opportunities and future developments of aircraft smart skin for SHM.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-23T11:00:55Z
      DOI: 10.1177/14759217211056831
       
  • Diagnosis of interior damage with a convolutional neural network using
           simulation and measurement data

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      Authors: Yanqing Bao, Sankaran Mahadevan
      Abstract: Structural Health Monitoring, Ahead of Print.
      Current deep learning applications in structural health monitoring (SHM) are mostly related to surface damage such as cracks and rust. Methods using traditional image processing techniques (such as filtering and edge detection) usually face difficulties in diagnosing internal damage in thicker specimens of heterogeneous materials. In this paper, we propose a damage diagnosis framework using a deep convolutional neural network (CNN) and transfer learning, focusing on internal damage such as voids and cracks. We use thermography to study the heat transfer characteristics and infer the presence of damage in the structure. It is challenging to obtain sufficient data samples for training deep neural networks, especially in the field of SHM. Therefore we use finite element (FE) computer simulations to generate a large volume of training data for the deep neural network, considering multiple damage shapes and locations. These computer-simulated data are used along with pre-trained convolutional cores of a sophisticated computer vision-based deep convolutional network to facilitate effective transfer learning. The CNN automatically generates features for damage diagnosis as opposed to manual feature generation in traditional image processing. Systematic parameter selection study is carried out to investigate accuracy versus computational expense in generating the training data. The methodology is demonstrated with an example of damage diagnosis in concrete, a heterogeneous material, using both computer simulations and laboratory experiments. The combination of FE simulation, transfer learning and experimental data is found to achieve high accuracy in damage localization with affordable effort.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-21T02:05:58Z
      DOI: 10.1177/14759217211056574
       
  • Efficient attention-based deep encoder and decoder for automatic crack
           segmentation

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      Authors: Dong H Kang, Young-Jin Cha
      Abstract: Structural Health Monitoring, Ahead of Print.
      Recently, crack segmentation studies have been investigated using deep convolutional neural networks. However, significant deficiencies remain in the preparation of ground truth data, consideration of complex scenes, development of an object-specific network for crack segmentation, and use of an evaluation method, among other issues. In this paper, a novel semantic transformer representation network (STRNet) is developed for crack segmentation at the pixel level in complex scenes in a real-time manner. STRNet is composed of a squeeze and excitation attention-based encoder, a multi head attention-based decoder, coarse upsampling, a focal-Tversky loss function, and a learnable swish activation function to design the network concisely by keeping its fast-processing speed. A method for evaluating the level of complexity of image scenes was also proposed. The proposed network is trained with 1203 images with further extensive synthesis-based augmentation, and it is investigated with 545 testing images (1280 × 720, 1024 × 512); it achieves 91.7%, 92.7%, 92.2%, and 92.6% in terms of precision, recall, F1 score, and mIoU (mean intersection over union), respectively. Its performance is compared with those of recently developed advanced networks (Attention U-net, CrackSegNet, Deeplab V3+, FPHBN, and Unet++), with STRNet showing the best performance in the evaluation metrics-it achieves the fastest processing at 49.2 frames per second.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-19T08:51:03Z
      DOI: 10.1177/14759217211053776
       
  • Modeling of an aircraft structural health monitoring sensor network for
           operational impact assessment

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      Authors: Kai-Daniel Büchter, Carlos Sebastia Saez, Dominik Steinweg
      Abstract: Structural Health Monitoring, Ahead of Print.
      Structural health monitoring (SHM) of aircraft components can improve maintenance operations, potentially reducing costs for inspections, unscheduled maintenance events, and unexpected delays. On the other hand, aircraft safety and net present value can be adversely influenced by false alarms, missed detections, system costs, and weight and power requirements of the SHM system. In order to gain a better understanding into the latter, we present a weight and power model for a sensor network, comprising sensors, interrogators, data collectors, and wiring. We assess the net benefit of using SHM in terms of reduced expenditure as function of network coverage, considering a corresponding potential in reducing the inspection effort.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-11T05:47:45Z
      DOI: 10.1177/14759217211048149
       
  • An optimized variational mode decomposition method and its application in
           vibration signal analysis of bearings

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      Authors: Jun Gu, Yuxing Peng, Hao Lu, Xiangdong Chang, Shuang Cao, Guoan Chen, Bobo Cao
      Abstract: Structural Health Monitoring, Ahead of Print.
      The performance of the rolling bearing of a spindle device is directly related to the safety and reliability of the operation of a mine hoist. To extract bearing vibration signal features effectively for fault diagnosis, a feature extraction method based on the parameter optimization of a variational mode decomposition (VMD) method and permutation entropy (PE) is proposed. In addition, a support vector machine (SVM) classifier is used to identify bearing fault types. An analogue signal is used to test the effect of noise and sampling frequency on VMD performance. Focused on the problem of the VMD method needing to determine the number of mode components K and a penalty factor α during the signal decomposition process, a genetic algorithm is used to optimize the parameter combination [K,α] with the minimum sample entropy as the indicator. By using mean squared error (MSE) and correlation coefficient, an evaluation indicator is constructed to determine the decomposition effects of the optimized VMD, centre frequency, empirical mode decomposition (EMD) and ensemble EMD (EEMD) methods. The normalized PE of the five mode components is used as an eigenvalue, which is used as the input parameter of the SVM. Two different experimental datasets are used to verify the effectiveness of the proposed method. The results show that the proposed method has better diagnostic accuracy than EMD, EEMD and a BP neural network in the case of limited samples and unknown sample inputs. It can provide a good reference for the diagnosis of a rolling bearing and has practical application value.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-07T09:25:36Z
      DOI: 10.1177/14759217211057444
       
  • A bolt loosening detection method based on patch antenna with overlapping
           sub-patch

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      Authors: Songtao Xue, Xianzhi Li, Liyu Xie, Zhuoran Yi, Guochun Wan
      Abstract: Structural Health Monitoring, Ahead of Print.
      Bolts are widely used in civil engineering, and the detection of bolt loosening is of great significance to ensure the safety of a structure. This paper introduces a new method for detecting bolt loosening using a customized detachable strain sensor based on a patch antenna. A patch antenna with overlapping sub-patch is proposed to measure the longitudinal elongation of the entire bolt shaft, indicating the loosening state of the bolt. When the bolt is fastened, the elongation of the bolt under tension will change the combined length of the underlying patch and the radiation sub-patch, consequently increasing or decreasing the resonant frequency of the antenna. The resonant frequency of the antenna can be measured by the vector network analyzer. Furthermore, with wireless interrogation of the strain sensor based on the patch antenna, the proposed method can also be used in the wireless detection of bolt loosening. The authors conducted a finite element analysis of the bolt and the electromagnetic simulations of the antenna. They designed the detection sensor and conducted a series of experimental tests to demonstrate how a bolt under different applied preloads can be effective and feasible under the proposed method.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-06T10:16:26Z
      DOI: 10.1177/14759217211055613
       
  • Optimal sensor placement to detect ruptures in pipeline systems subject to
           uncertainty using an Adam-mutated genetic algorithm

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      Authors: Chungeon Kim, Hyunseok Oh, Byung Chang Jung, Seok Jun Moon
      Abstract: Structural Health Monitoring, Ahead of Print.
      Pipelines in critical engineered facilities, such as petrochemical and power plants, conduct important roles of fire extinguishing, cooling, and related essential tasks. Therefore, failure of a pipeline system can cause catastrophic disaster, which may include economic loss or even human casualty. Optimal sensor placement is required to detect and assess damage so that the optimal amount of resources is deployed and damage is minimized. This paper presents a novel methodology to determine the optimal location of sensors in a pipeline network for real-time monitoring. First, a lumped model of a small-scale pipeline network is built to simulate the behavior of working fluid. By propagating the inherent variability of hydraulic parameters in the simulation model, uncertainty in the behavior of the working fluid is evaluated. Sensor measurement error is also incorporated. Second, predefined damage scenarios are implemented in the simulation model and estimated through a damage classification algorithm using acquired data from the sensor network. Third, probabilistic detectability is measured as a performance metric of the sensor network. Finally, a detectability-based optimization problem is formulated as a mixed integer non-linear programming problem. An Adam-mutated genetic algorithm (AMGA) is proposed to solve the problem. The Adam-optimizer is incorporated as a mutation operator of the genetic algorithm to increase the capacity of the algorithm to escape from the local minimum. The performance of the AMGA is compared with that of the standard genetic algorithm. A case study using a pipeline system is presented to evaluate the performance of the proposed sensor network design methodology.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-06T09:58:35Z
      DOI: 10.1177/14759217211056557
       
  • Restoration of missing structural health monitoring data using
           spatiotemporal graph attention networks

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      Authors: Jin Niu, Shunlong Li, Zhonglong Li
      Abstract: Structural Health Monitoring, Ahead of Print.
      For structural health monitoring systems with many low-cost sensors, missing data caused by sensor faults, power supply interruptions and data transmission errors are almost inevitable, significantly affecting structural diagnosis and evaluation. Considering the inherent spatial and temporal correlations in the sensor network, this study proposes a spatiotemporal graph attention network for restoration of missing data. The proposed model was stacked with a graph convolutional layer and several spatiotemporal blocks composed of spatial and temporal layers. The monitoring data of normal sensors were first mapped to all sensors through the graph convolutional layer, and attention mechanisms were used in the spatiotemporal blocks to model the spatial dependencies of sensors and the temporal dependencies of time steps, respectively. The extracted spatiotemporal features were assembled through a fully connected layer to reconstruct the missing signals. In this study, both homogeneous and heterogeneous monitoring items were used to calculate the spatial attention coefficients. The data restoration accuracy with and without the multi-source data fusion was discussed. Application on a long-span cable-stayed bridge to restore missing cable forces demonstrates that spatiotemporal attention modelling can achieve satisfactory restoring accuracy without any prior analysis.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-06T09:44:16Z
      DOI: 10.1177/14759217211056832
       
  • Immunity of the second harmonic shear horizontal waves to adhesive
           nonlinearity for breathing crack detection

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      Authors: Fuzhen Wen, Shengbo Shan, Li Cheng
      Abstract: Structural Health Monitoring, Ahead of Print.
      High-order harmonic guided waves are sensitive to micro-scale damage in thin-walled structures, thus, conducive to its early detection. In typical autonomous structural health monitoring (SHM) systems activated by surface-bonded piezoelectric wafer transducers, adhesive nonlinearity (AN) is a non-negligible adverse nonlinear source that can overwhelm the damage-induced nonlinear signals and jeopardize the diagnosis if not adequately mitigated. This paper first establishes that the second harmonic shear horizontal (second SH) waves are immune to AN while exhibiting strong sensitivity to cracks in a plate. Capitalizing on this feature, the feasibility of using second SH waves for crack detection is investigated. Finite element (FE) simulations are conducted to shed light on the physical mechanism governing the second SH wave generation and their interaction with the contact acoustic nonlinearity (CAN). Theoretical and numerical results are validated by experiments in which the level of the AN is tactically adjusted. Results show that the commonly used second harmonic S0 (second S0) mode Lamb waves are prone to AN variation. By contrast, the second SH0 waves show high robustness to the same degree of AN changes while preserving a reasonable sensitivity to breathing cracks, demonstrating their superiority for SHM applications.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-06T02:55:10Z
      DOI: 10.1177/14759217211057138
       
  • Aging state detection of viscoelastic sandwich structure using redundant
           second generation wavelet packet transform and fuzzy support vector data
           description

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      Authors: Jinxiu Qu, Changquan Shi, Jinzhu Guo, Xiaowei Shi, Jiaqi Huang, Wei Cao, Jinjuan Sun
      Abstract: Structural Health Monitoring, Ahead of Print.
      Viscoelastic sandwich structure plays an important role in mechanical equipment, nevertheless viscoelastic material inevitably suffers from gradual aging. For guaranteeing the operation safety of mechanical equipment, it is urgent to perform the aging state detection of viscoelastic sandwich structure with vibration response signal analysis. However, the structural vibration response signal is non-stationary and its variation caused by the structural aging state change is very puny, and the abnormal state samples is lacking. The vibration-based structural aging state detection has become a challenging task. Therefore, a novel method based on redundant second generation wavelet packet transform (RSGWPT) and fuzzy support vector data description (FSVDD) is proposed for this task. For extracting sensitive aging feature information, RSGWPT is introduced to process the structural vibration response signal, and multiple energy features are extracted from the frequency-band signals to reflect structural aging state change. For accurate and automatic aging state identification, by fusing fuzzy theory, FSVDD only uses the normal state samples for training and can identify the abnormal severity degrees is developed to identify the structural aging states. The proposed method is applied on a viscoelastic sandwich structure to validate its effectiveness, and different structural aging states are created through the accelerated aging of viscoelastic material. The analysis results show the outstanding performance of the proposed method.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-06T01:57:43Z
      DOI: 10.1177/14759217211057587
       
  • Health monitoring of sandwich composites with auxetic core subjected to
           indentation tests using acoustic emission

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      Authors: Khawla Essassi, Jean-Luc Rebiere, Abderrahim EL Mahi, Mohamed Amine Ben souf, Anas Bouguecha, Mohamed Haddar
      Abstract: Structural Health Monitoring, Ahead of Print.
      The quasi-static indentation behavior of an eco-sandwich composite with auxetic core consisting of polylactic acid reinforced with flax fibers will be discussed in this article. The structures involved in the test were manufactured using 3D printing technique. Four configurations with different number of cells in the core, were tested. It is found that sandwiches with high number of cells are stiffer and dissipate more energy. Experimental tests were monitored with acoustic emission technique in order to detect the appearance and the evolution of damage behavior. An unsupervised pattern recognition algorithm was used to post process the acoustic emission signals. The classification is conducted using k-means algorithm. Results show that there are three different classes of events for each configuration, which are the core cracking, the matrix cracking and the fiber/matrix debonding. The evaluation of the contribution of each damage mechanism on the total amount of failure was deduced according to the amplitude range, the cumulative number of hits and the acoustic energy activity. Furthermore, macroscopic and microscopic observations were performed in order to correlate acoustic emission classes with the damage mechanisms observed.
      Citation: Structural Health Monitoring
      PubDate: 2021-12-02T12:17:59Z
      DOI: 10.1177/14759217211053991
       
  • Bridge pier scour level quantification based on output-only Kalman
           filtering

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      Authors: Esmaeil Ghorbani, Dagmar Svecova, Douglas J Thomson, Young-Jin Cha
      Abstract: Structural Health Monitoring, Ahead of Print.
      Soil scour near a bridge pier foundation is one of the leading causes of bridge failures. Traditional vibration-based scour monitoring methods are nearly incapable of quantifying scour levels using a single acceleration response without knowledge of excitation information. In this paper, a new output-only scour level prediction method is introduced via the integration of an unscented Kalman filter (UKF), random decrement (RD), and newly derived continuous Euler–Bernoulli beam addressing river water, traffic loads, and the linear and nonlinear behavior of sediments around the pier as external effects. We conducted extensive simulation studies and applied the method to an existing medium-span bridge with a steel girder and concrete deck in service in the province of Manitoba, Canada. These studies show that our proposed method can accurately estimate scour levels using only one accelerometer, which was validated with an independent bathymetric survey of the soil level at the pier foundation. Furthermore, three different linear and nonlinear soil profiles representing the soil behavior around the pier were also investigated as case studies in the scour level estimation process. The results confirm that a cubic function exhibits the best performance in quantifying the scour level around bridge piers.
      Citation: Structural Health Monitoring
      PubDate: 2021-11-25T08:08:31Z
      DOI: 10.1177/14759217211053781
       
  • A novel autonomous strategy for multi-bolt looseness detection using smart
           glove and Siamese double-path CapsNet

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      Authors: Furui Wang
      Abstract: Structural Health Monitoring, Ahead of Print.
      Recently, the issue of bolt looseness has attracted more attention due to its severe consequences. Among different methods for bolt looseness detection, the active sensing method that is based on stress wave signals is preferred since it is low cost and high robust. However, current active sensing method depends on permanent contact sensors, which may be impractical. Moreover, the investigation of multi-bolt looseness detection via the active sensing is very limited so far. With the above deficiency in mind, we propose a new robotic-assisted active sensing method based on our newly designed PZT-enabled smart gloves (SGs) and position-based visual servoing (PBVS) technique. Particularly, another main contribution is that we develop a new Siamese CapsNet to classify stress wave signals under different cases for multi-bolt looseness detection. Compared to machine learning (ML) and traditional deep learning techniques such as Convolutional Neural Networks (CNN), the proposed Siamese CapsNet model can achieve better performance and realize the recognition of signals that is never used during the training, which is impossible for common classification methods. Finally, an experiment is conducted to verify the effectiveness of the proposed method and Siamese CapsNet, which can guide future research significantly.
      Citation: Structural Health Monitoring
      PubDate: 2021-11-24T08:11:52Z
      DOI: 10.1177/14759217211054575
       
  • Effect of fiber dosage on water permeability using a newly designed
           apparatus and crack monitoring of steel fiber–reinforced concrete under
           direct tensile loading

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      Authors: Zahoor Hussain, Zhang Pu, Abasal Hussain, Shakeel Ahmed, Atta Ullah Shah, Aitzaz Ali, Azhar Ali
      Abstract: Structural Health Monitoring, Ahead of Print.
      Cracks in concrete structures have always been the main reason to allow the aggressive and harmful agents to infringe the concrete resulting in its deterioration and decreasing lifespan. In the present study, the water permeability of the cracked concrete has been investigated. The consequences of cracking on the durability and endurance of concrete were also studied. A state-of-the-art permeability setup was designed to measure the water flow in normal and fiber-reinforced concrete under direct tensile loading. The setup was convenient for determining the average stress applied to the concrete specimens and simultaneously the maximum crack opening. Furthermore, the effect of fiber content on the cracking geometry (tortuosity and roughness) was evaluated by incorporating the coordinate data of the cracked surface using a 3D sensor-based laser scanning data acquisition system. To understand the effect of fiber content on the cracking geometry (tortuosity and roughness), the acquired data were then analyzed. Test results show that the designed setup is suitable to measure the water permeability under direct tensile loading. Water permeability decreased upon increasing the steel fiber dosage. Besides, the results show that tortuosity decreased while surface roughness increased with the fiber dosage increment. Promising preliminary results indicated that there is an inverse relationship between surface roughness and water permeability. The crack sensing setup successfully monitored the crack.
      Citation: Structural Health Monitoring
      PubDate: 2021-11-23T11:13:53Z
      DOI: 10.1177/14759217211052855
       
  • Real-time quantitative evaluation on the cable damage of cable-stayed
           bridges using the correlation between girder deflection and temperature

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      Authors: Gaoxin Wang, Jingshu Shao, Weizhou Xu, Zhaoxing Dong, Bin Chen, Xinjun Dai
      Abstract: Structural Health Monitoring, Ahead of Print.
      Stayed cable is an important prestress-bearing component in cable-stayed bridges, and the cable damage will seriously threaten bridge safety. In this research, the method of real-time quantitative evaluation on cable damage is proposed through monitoring data analysis, correlation analysis, damage evaluation analysis, and validation analysis. Monitoring data analysis shows that temperature has a good linear relationship with girder deflection and cable force. Correlation analysis shows that this relationship is well described by a time-varying multiple linear regression model. In damage evaluation analysis, a new damage index is proposed for real-time quantitative evaluation. Each stay cable has a corresponding damage index, and a large value of damage index indicates a serious damage. The results of experiment and finite element analysis show that the evaluation error of this damage index is very small, which is feasible for real-time quantitative evaluation. This method can provide valuable reference for real-time quantitative evaluation on cable damage of cable-stayed bridges.
      Citation: Structural Health Monitoring
      PubDate: 2021-10-31T11:53:37Z
      DOI: 10.1177/14759217211035048
       
  • Analysis for full face mechanical behaviors through spatial deduction
           model with real-time monitoring data

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      Authors: Xuyan Tan, Yuhang Wang, Bowen Du, Junchen Ye, Weizhong Chen, Leilei Sun, Liping Li
      Abstract: Structural Health Monitoring, Ahead of Print.
      Mechanical analysis for the full face of tunnel structure is crucial to maintain stability, which is a challenge in classical analytical solutions and data analysis. Along this line, this study aims to develop a spatial deduction model to obtain the full-faced mechanical behaviors through integrating mechanical properties into pure data-driven model. The spatial tunnel structure is divided into many parts and reconstructed in a form of matrix. Then, the external load applied on structure in the field was considered to study the mechanical behaviors of tunnel. Based on the limited observed monitoring data in matrix and mechanical analysis results, a double-driven model was developed to obtain the full-faced information, in which the data-driven model was the dominant one and the mechanical constraint was the secondary one. To verify the presented spatial deduction model, cross-test was conducted through assuming partial monitoring data are unknown and regarding them as testing points. The well agreement between deduction results with actual monitoring results means the proposed model is reasonable. Therefore, it was employed to deduct both the current and historical performance of tunnel full face, which is crucial to prevent structural disasters.
      Citation: Structural Health Monitoring
      PubDate: 2021-10-15T07:18:44Z
      DOI: 10.1177/14759217211044803
       
  • Static strain-based identification of extensive damages in thin-walled
           structures

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      Authors: Nicholas E. Silionis, Konstantinos N. Anyfantis
      Abstract: Structural Health Monitoring, Ahead of Print.
      Interest has been expressed during the past few years toward incorporating structural health monitoring (SHM) systems in ship hull structures for detecting damages that cause significant load-carrying reductions and subsequent load redistributions. The guiding principle of the damage identification strategy considered in this work is based upon measuring, through a limited number of sensors, the static strain redistributions caused by an extensive damage. The problem is tackled as a statistical pattern recognition one, and therefore, methods sourcing from machine learning (ML) are applied. The SHM strategy is both virtually and experimentally applied to a thin-walled prismatic geometry that represents an idealized hull form solely subjected to principal bending stresses (sagging/hogging). Damage modes causing extensive stress redistribution, are abstractly represented by a circular discontinuity. The damage identification problem is treated in a hierarchical order, initialized by damage detection and moving to an increasingly more localized prediction of the damage location. Training datasets for the ML tools are generated from numerical finite element simulations. Measurement uncertainty is propagated in the theoretical strains by information inferred from experimental data. Two different sensor architectures were assessed. An experimental programme is performed for testing the accuracy of the proposed damage identification strategy, yielding promising results and providing valuable insights.
      Citation: Structural Health Monitoring
      PubDate: 2021-10-15T06:38:21Z
      DOI: 10.1177/14759217211050605
       
  • Interpretable convolutional sparse coding method of Lamb waves for damage
           identification and localization

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      Authors: Han Zhang, Jing Lin, Jiadong Hua, Tong Tong
      Abstract: Structural Health Monitoring, Ahead of Print.
      Lamb wave-based damage identification and localization methods hold the potential for nondestructive evaluation and structural health monitoring. Dispersive and multimodal characteristics lead to complicated Lamb wave signals that are challenging to be analyzed. Deep learning architectures could identify damage-related features effectively. Convolutional neural network (CNN) is a promising architecture that has been widely applied in recent years. However, this data-driven approach still lacks a certain degree of physical interpretability and requires a large number of parameters. In this article, an interpretable Lamb wave convolutional sparse coding (LW-CSC) method is proposed for structural damage identification and localization. First, toneburst signals at different center frequencies are considered in the first convolutional layer. The network convolves the waveforms with a set of parametrized functions that implement band-pass filters, which performs more physical interpretability compared to conventional CNN model. Subsequently, the damage features are extracted according to the multi-layer iterative soft thresholding algorithm for multi-layer CSC model, which could realize a deeper network without adding parameters unlike CNN. Finally, Lamb wave-based damage localization is visualized using an imaging algorithm. The experimental results demonstrate that the proposed method not only enables improvement of the classification accuracy but also achieves structural damage localization accurately.
      Citation: Structural Health Monitoring
      PubDate: 2021-10-07T01:32:25Z
      DOI: 10.1177/14759217211044806
       
  • Hybrid artificial intelligence-based inference models for accurately
           predicting dam body displacements: A case study of the Fei Tsui dam

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      Authors: Min-Yuan Cheng, Minh-Tu Cao, I-Feng Huang
      Abstract: Structural Health Monitoring, Ahead of Print.
      Surveillance is a critical activity in monitoring the operation condition and safety of dams. This study reviewed the historical monitoring data of the Fei Tsui dam to determine possible influential factors for the dam body displacement and then evaluated the influencing degree of these factors by using correlation analysis. Thus, the key influential factors were identified objectively and further chosen as the input variables for numerous artificial intelligence (AI)-based inference models, including single machine learning techniques (support vector machine (SVM), artificial neural networks) and hybrid AI models. The models were trained and tested with 4722 real data retrieved in 11 years from the monitoring devices installed on elements of the dam, and then generated their respective inferred dam body displacement values. The results revealed that the adaptive time-dependent evolutionary least squares SVM model had the greatest performance by providing the lowest values of prediction errors in terms of mean absolute percentage error (MAPE = 8.14%), root mean square error (RMSE = 1.08 cm), and coefficient of determination (R = 0.993). The analysis results endorsed that the hybrid AI model could be an efficient tool to early produce accurate warnings of the dam displacements.
      Citation: Structural Health Monitoring
      PubDate: 2021-10-01T10:21:01Z
      DOI: 10.1177/14759217211044116
       
  • Acoustic emission waveforms for damage monitoring in composite materials:
           Shifting in spectral density, entropy and wavelet packet transform

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      Authors: Claudia Barile, Caterina Casavola, Giovanni Pappalettera, Vimalathithan Paramsamy Kannan
      Abstract: Structural Health Monitoring, Ahead of Print.
      Signal-based acoustic emission data are analysed in this research work for identifying the damage modes in carbon fibre–reinforced plastic (CFRP) composites. The research work is divided into three parts: analysis of the shifting in the spectral density of acoustic waveforms, use of waveform entropy for selecting the best wavelet and implementation of wavelet packet transform (WPT) for identifying the damage process. The first two methodologies introduced in this research work are novel. Shifting in the spectral density is introduced in analogous to ‘flicker noise’ which is popular in the field of waveform processing. The entropy-based wavelet selection is refined by using quadratic Renyi’s entropy and comparing the spectral energy of the dominating frequency band of the acoustic waveforms. Based on the method, ‘dmey’ wavelet is selected for analysing the waveforms using WPT. The slope values of the shifting in spectral density coincide with the results obtained from WPT in characterising the damage modes. The methodologies introduced in this research work are promising. They serve the purpose of identifying the damage process effectively in the CFRP composites.
      Citation: Structural Health Monitoring
      PubDate: 2021-10-01T10:15:46Z
      DOI: 10.1177/14759217211044692
       
  • Damage detection and characterization of a scaled model steel truss bridge
           using combined complete ensemble empirical mode decomposition with
           adaptive noise and multiple signal classification approach

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      Authors: Asma A Mousavi, Chunwei Zhang, Sami F Masri, Gholamreza Gholipour
      Abstract: Structural Health Monitoring, Ahead of Print.
      This study aims to investigate the performance of a new damage detection method proposed based on the combination of two signal processing techniques which are complete ensemble empirical mode decomposition with adaptive noise and multiple signal classification (CEEMDAN-MUSIC). The proposed damage detection approach begins with determining the power density spectrum, namely, the pseudospectrum, from the acceleration response of a structure. Then, the CEEMDAN algorithm is used to decompose the vibration signal into a set of intrinsic mode functions (IMFs). Furthermore, the MUSIC algorithm is applied to the first IMF of the processed signal to determine the frequency pseudospectrum, prior to and post the damage states of the structure. The effectiveness of the proposed methodology is experimentally validated using a laboratory-scale model of a steel truss bridge exposed to a white noise excitation. The damage states of the truss bridge are implemented by replacing a specified diagonal element with reduced cross-sectional stiffness. The experimental results demonstrate the superiority of the CEEMDAN-MUSIC method in comparison with the performance of pure MUSIC and traditional frequency domain techniques. The advantages of the proposed technique are also discussed in terms of identifying the presence of the damage, addressing its location, and quantifying the damage levels which are summarized as the damage detection and characterization.
      Citation: Structural Health Monitoring
      PubDate: 2021-09-28T09:25:33Z
      DOI: 10.1177/14759217211045901
       
  • Pipeline leak detection and corrosion monitoring based on a novel FBG
           pipe-fixture sensor

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      Authors: Jiajian Wang, Liang Ren, Ziguang Jia, Tao Jiang, Guo-xin Wang
      Abstract: Structural Health Monitoring, Ahead of Print.
      Pipeline leakage and pipeline internal corrosion are two serious threats to pipeline safety operation. In this study, based on the measurement theory of pipeline internal diameter variation and a numerical simulation analysis of existing sensor shortcomings, a novel FBG pipe-fixture sensor is designed and developed to detect pipeline leakage and monitor pipeline internal corrosion simultaneously. A calibration test is conducted, and the test results indicate that the FBG pipe-fixture sensor solves the problem of a large difference between the test values and the theoretical values and eliminates the limitation of measurement range. To validate the actual performance of the sensor, a pipeline leak detection test and a pipeline internal corrosion monitoring test are, respectively, conducted on a steel pipe leak test platform and a steel pipe with different simulated corrosion levels. The test results demonstrate that the FBG pipe-fixture sensor can effectively detect pipe leakage and decrease the localization error of the leakage point to within 1 m. The sensor can also effectively monitor pipe internal corrosion and evaluate the internal corrosion degree with a measurement accuracy of 0.1 mm. The novel FBG pipe-fixture sensor may be a potential sensor for pipeline leak detection and pipeline internal corrosion monitoring.
      Citation: Structural Health Monitoring
      PubDate: 2021-09-22T06:16:19Z
      DOI: 10.1177/14759217211044966
       
  • Fast Tomography: A greedy, heuristic, mesh size–independent methodology
           for local velocity reconstruction for AE waves in distance decaying
           environment in semi real-time

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      Authors: Avik Kumar Das, Christopher KY Leung
      Abstract: Structural Health Monitoring, Ahead of Print.
      Tomographic reconstruction is an important step toward visualization, identification and quantification of local damage through of structural elements. We have developed mathematical guiding principles for passive wave tomography. We have then utilized these guiding principles to develop a novel technique: Fast Tomography for computational and information efficiency in tomographic reconstruction with passive stress waves in a distance decaying (sensing) environment. In fast tomography, (i) a node-independent travel path is developed for computational efficiency and (ii) Apriori ranking of AE events using power spectral entropy (PSE) of the AE waveform to distinguish waveforms with high information content for tomographic reconstruction for information efficiency are proposed. Fast Tomography was studied theoretically and experimentally to benchmark the proposed method in terms of computational and information efficiency. Our algorithm provides a significant (>100x) improvement of computational efficiency over an existing approach. And a PSE-based ranking system for AE events enhances information efficiency by 50% as compared to a non-ranked system. Finally, we have validated the application of our method with intractably generated AE events in an accelerated damage test of a steel fiber–reinforced concrete beam.
      Citation: Structural Health Monitoring
      PubDate: 2021-09-21T04:31:30Z
      DOI: 10.1177/14759217211036881
       
  • A fast approach for acoustic source localization on a thin spherical shell

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      Authors: Zixian Zhou, Zhiwen Cui, Tribikram Kundu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Thin spherical shell structures are wildly used as pressure vessels in the industry because of their property of having equal in-plane normal stresses in all directions. Since very large pressure difference between the inside and outside of the wall exists, any formation of defects in the pressure vessel wall has a huge safety risk. Therefore, it is necessary to quickly locate the area where the defect maybe located in the early stage of defect formation and make repair on time. The conventional acoustic source localization techniques for spherical shells require either direction-dependent velocity profile knowledge or a large number of sensors to form an array. In this study, we propose a fast approach for acoustic source localization on thin isotropic and anisotropic spherical shells. A solution technique based on the time difference of arrival on a thin spherical shell without the prior knowledge of direction-dependent velocity profile is provided. With the help of “L”-shaped sensor clusters, only 6 sensors are required to quickly predict the acoustic source location for anisotropic spherical shells. For isotropic spherical shells, only 4 sensors are required. Simulation and experimental results show that this technique works well for both isotropic and anisotropic spherical shells.
      Citation: Structural Health Monitoring
      PubDate: 2021-09-02T06:04:05Z
      DOI: 10.1177/14759217211041902
       
  • Monitoring of self-healing in concrete with micro-capsules using a
           combination of air-coupled surface wave and computer-vision techniques

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      Authors: Eunjong Ahn, Hyunjun Kim, Seongwoo Gwon, Sung-Rok Oh, Cheol-Gyu Kim, Sung-Han Sim, Myoungsu Shin
      Abstract: Structural Health Monitoring, Ahead of Print.
      This study mainly aims to investigate the applicability of the combination of air-coupled surface-wave and computer-vision techniques to the evaluation of self-healing in in situ concrete members. Small-scale beam specimens were made from ordinary concrete and concretes with solid- and liquid-type capsules; the capsules were employed as self-healing agents. To monitor the crack healing progress, surface-wave tests using an air-coupled transducer and contact receivers were conducted on each specimen in uncracked, cracked, and healed conditions after 7, 14, 28, and 63 days of water immersion. Additionally, a computer-vision technique involving image binarization and registration was applied to measure high-resolution crack information. The specimens containing the micro-capsules showed superior healing performance compared to the ordinary concrete specimens. After 63 days of self-healing, the spectral energy transmission ratio increased up to about 80% of the uncracked, while the crack area decreased up to about 94% of the fully cracked. The healing rate was estimated using the change in spectral energy transmission ratio strongly correlated with that estimated using the change in crack area.
      Citation: Structural Health Monitoring
      PubDate: 2021-08-31T04:33:25Z
      DOI: 10.1177/14759217211041002
       
  • Wave amplitude of embedded ultrasonic transducer-based damage monitoring
           of concrete due to steel bar corrosion

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      Authors: Shunquan Zhang, Zijian Jia, Yuanliang Xiong, Ruilin Cao, Yamei Zhang, Nemkumar Banthia
      Abstract: Structural Health Monitoring, Ahead of Print.
      In this research, four embedded ultrasonic piezoelectric transducers were combined to form cross pair and opposite pair monitoring schemes for continuously monitoring the damage to different strength grades of concrete caused by the corrosion of reinforcements under accelerated corrosion conditions. The damage process was analyzed by combining the electrochemical effects of steel corrosion, that is, half-cell potential and galvanic current tests. Results show that the embedded ultrasonic transducer method can detect damage of concrete during steel corrosion and that each stage of damage can be determined from the plots of ultrasonic transducer data versus corrosion rate. The results further indicate that a combination of cross pair and opposite pair testing methods can more comprehensively reflect the damage to concrete caused by the expansion of corrosion of steel bars, than a single testing method. Since electrochemical testing can only depict the corrosion state of steel rebars, it is beneficial to use embedded ultrasonic measurements to monitor the damage process of concrete. The differences in damage between different strength grades of concrete, that is, the resistance to corrosion of steel bars and brittle failure, can be obtained from the plots of ultrasonic transducer data.
      Citation: Structural Health Monitoring
      PubDate: 2021-08-30T10:50:15Z
      DOI: 10.1177/14759217211041706
       
  • Direct-write piezoelectric coating transducers in combination with
           discrete ceramic transducer and laser pulse excitation for ultrasonic
           impact damage detection on composite plates

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      Authors: Marilyne Philibert, Shuting Chen, Voon-Kean Wong, Weng Heng Liew, Kui Yao, Constantinos Soutis, Matthieu Gresil
      Abstract: Structural Health Monitoring, Ahead of Print.
      In this work, direct-write piezoelectric transducers (DWTs) were made by spraying piezoelectric poly(vinylidene fluoride-co-trifluoroethylene) coating with comb-shaped electrodes on carbon fibre reinforced polymer (CFRP) plates for drop weight impact damage detection. Their ability and performance were investigated and compared to discrete piezoelectric lead zirconate titanate (PZT) ceramic transducers that were adhesively bonded on the same CFRP plate. Guided wave signals were acquired with different combinations of actuator-sensor involving DWT, PZT and laser ultrasonic excitation, in pitch-catch configuration. DWTs allowed consistency and simplified signal interpretation due to an effective mode selection (A0 or S0 mode) with wavelengths of 10 and 12 mm. PZTs generated stronger but much more complex signals and mode selection with a larger wavelength (20 mm). The configuration with PZT as actuator and DWT as receiver showed the highest signal amplitude changes of A0 or S0 mode, allowing efficient detection of damage introduced by a 31 J impact. Further ultrasonic B- and C-scans revealed a 27 mm long crack on the plate’s backside developed in addition to internal cracks and delaminations of about 34 mm in length. For realizing contactless ultrasound excitation, a neodymium-doped yttrium aluminium garnet laser (wavelength of 1064 nm, 5.4 ns pulses) was used to replace the surface-mounted brittle PZT. The combination of the broadband laser excitation with the DWTs as sensors achieved more reliable damage detection than equivalent PZTs, attributed to DWT’s effective single mode selection. In addition to reduced weight, the polymeric coated DWTs allow large area implementation (scaling up), even on curved surfaces due to their flexibility and conformability, in contrast to adhesively bonded discrete transducers.
      Citation: Structural Health Monitoring
      PubDate: 2021-08-30T03:19:44Z
      DOI: 10.1177/14759217211040719
       
  • An innovative machine learning based framework for water distribution
           network leakage detection and localization

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      Authors: Xudong Fan, Xiong (Bill) Yu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Leakages in the underground water distribution networks (WDNs) waste over 1 billion gallon of water annually in the US and cause significant socio-economic loss to our communities. However, detecting and localization leakage in a WDN remains a challenging technical problem despite of significant progresses in this domain. The progresses in machine learning (ML) provides new ways to identify the leakage by data-driven methods. However, in-service WDNs are short of labeled data under leaking conditions, which makes it infeasible to use common ML models. This study proposed a novel machine learning (ML)-based framework for WDN leak detection and localization. This new framework, named clustering-then-localization semi-supervised learning (CtL-SSL), uses the topological relationship of WDN and its leakage characteristics for WDN partition and sensors placement, and subsequently utilizes the monitoring data for leakage detection and leakage localization. The CtL-SSL framework is applied to two testbed WDNs and achieves 95% leakage detection accuracy and around 83% final leakage localization accuracy by use of unbalanced data with less than 10% leaking data. The developed CtL-SSL framework advances the leak detection strategy by alleviating the data requirements, guiding optimal sensor placement, and locating leakage via WDN leakage zone partition. It features excellent scalability, extensibility, and upgradeability for applications to various types of WDNs. It will provide valuable a tool in sustainable management of the WDNs.
      Citation: Structural Health Monitoring
      PubDate: 2021-08-30T03:19:42Z
      DOI: 10.1177/14759217211040269
       
  • Hyperspectral imaging for the elimination of visual ambiguity in corrosion
           detection and identification of corrosion sources

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      Authors: Dayakar N Lavadiya, Hizb Ullah Sajid, Ravi K Yellavajjala, Xin Sun
      Abstract: Structural Health Monitoring, Ahead of Print.
      The similarity in the hue of corroded surfaces and coated surfaces, dust, vegetation, etc. leads to visual ambiguity which is challenging to eliminate using existing image classification/segmentation techniques. Furthermore, existing methods lack the ability to identify the source of corrosion, which plays a vital role in framing the corrosion mitigation strategies. The goal of this study to employ hyperspectral imaging (1) to detect corroded surfaces under visually ambiguous scenarios and (2) identify the source of corrosion in such scenarios. To this end, three different corrosive media, namely, (1) 1M hydrochloric acid (HCl), 2) 3.5 wt.% sodium chloride solution (NaCl), and (3) 3 wt.% sodium sulfate solution (Na2SO4), are employed to generate chemically distinctive corroded surfaces. The hyperspectral imaging sensor is employed to obtain the visible and near infrared (VNIR) spectra (397 nm–1004 nm) reflected by the corroded/coated surfaces. The intensity of the reflectance in various spectral bands are considered as the descriptive features in this study, and the training and test datasets were generated consisting of 35,000 and 15,000 data points, respectively. SVM classifier is trained and then its efficacy on the test data is assessed. Furthermore, validation datasets are employed and the generalization ability of the trained SVM classifier is verified. The results from this study revealed that the SVM classifier achieved an overall accuracy of 94% with the misclassifications of 18% and 13% in the case of NaCl and Na2SO4 corrosion, respectively. Reflectance spectra obtained in the VNIR region was found to eliminate the visual ambiguity between the corroded and coated surfaces and, identify the source of corrosion accurately. Further, the range of key wavelengths of the spectra that play an important role in the distinguishability of coating and chemically distinctive corroded surface were identified to be 500–520 nm, 660–680 nm, 760–770 nm, and 830–850 nm.
      Citation: Structural Health Monitoring
      PubDate: 2021-08-28T11:32:41Z
      DOI: 10.1177/14759217211041690
       
  • Corrigendum to Predictive information and maintenance optimization based
           on decision theory: a case study considering a welded joint in an offshore
           wind turbine support structure

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      Abstract: Structural Health Monitoring, Ahead of Print.

      Citation: Structural Health Monitoring
      PubDate: 2021-08-27T06:25:06Z
      DOI: 10.1177/14759217211040385
       
  • A probabilistic method for structural integrity assurance based on damage
           detection structural health monitoring data

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      Authors: Michael Siu Hey Leung, Joseph Corcoran
      Abstract: Structural Health Monitoring, Ahead of Print.
      The value of using permanently installed monitoring systems for managing the life of an engineering asset is determined by the confidence in its damage detection capabilities. A framework is proposed that integrates detection data from permanently installed monitoring systems with probabilistic structural integrity assessments. Probability of detection (POD) curves are used in combination with particle filtering methods to recursively update a distribution of postulated defect size given a series of negative results (i.e. no defects detected). The negative monitoring results continuously filter out possible cases of severe damage, which in turn updates the estimated probability of failure. An implementation of the particle filtering method that takes into account the effect of systematic uncertainty in the detection capabilities of a monitoring system is also proposed, addressing the problem of whether negative measurements are simply a consequence of defects occurring outside the sensors field of view. A simulated example of fatigue crack growth is used to demonstrate the proposed framework. The results demonstrate that permanently installed sensors with low susceptibility to systematic effects may be used to maintain confidence in fitness-for-service while relying on fewer inspections. The framework provides a method for using permanently installed sensors to achieve continuous assessments of fitness-for-service for improved integrity management.
      Citation: Structural Health Monitoring
      PubDate: 2021-08-18T12:23:00Z
      DOI: 10.1177/14759217211038881
       
  • Machine learning and structural health monitoring overview with emerging
           technology and high-dimensional data source highlights

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      Authors: Arman Malekloo, Ekin Ozer, Mohammad AlHamaydeh, Mark Girolami
      Abstract: Structural Health Monitoring, Ahead of Print.
      Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past’s applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity.
      Citation: Structural Health Monitoring
      PubDate: 2021-08-16T03:24:10Z
      DOI: 10.1177/14759217211036880
       
  • A feature learning-based method for impact load reconstruction and
           localization of the plate-rib assembled structure

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      Authors: Tao Chen, Liang Guo, Andongzhe Duan, Hongli Gao, Tingting Feng, Yichen He
      Abstract: Structural Health Monitoring, Ahead of Print.
      Impact load is the load that machines frequently experienced in engineering applications. Its time-history reconstruction and localization are crucial for structural health monitoring and reliability analysis. However, when identifying random impact loads, conventional inversion methods usually do not perform well because of complex formula derivation, infeasibility of nonlinear structure, and ill-posed problem. Deep learning methods have great ability of feature learning and nonlinear representation as well as comprehensive regularization mechanism. Therefore, a new feature learning-based method is proposed to conduct impact load reconstruction and localization. The proposed method mainly includes two parts. The first part is designed to reconstruct impact load, named convolutional-recurrent encoder–decoder neural network (ED-CRNN). The other part is constructed to localize impact load, called deep convolutional-recurrent neural network (DCRNN). The ED-CRNN utilizes the one-dimensional (1-D) convolutional encoder–decoder to obtain low-dimension feature representations of input signals. Two long short-term memory (LSTM) layers and a bidirectional LSTM (BiLSTM) layer are uniformly distributed in this network to learn the relationship between input features and the output load in time steps. The DCRNN is constructed mainly by two 1-D convolutional neural network (CNN) layers and two BiLSTM layers to learn high-hidden-level spatial as well as temporal features. The fully connected layers are placed at the end to localize an impact load. The effectiveness of the proposed method was demonstrated by two numerical studies and two experiments. The results reveal that the proposed method has the ability to accurately and quickly reconstruct and localize the impact load of complex assembled structure. Furthermore, the performance of the DCRNN is related to the number of sensors and the architecture of the network. Meanwhile, the strategy of alternating layout is proposed to reduce the number of training locations.
      Citation: Structural Health Monitoring
      PubDate: 2021-08-13T03:41:39Z
      DOI: 10.1177/14759217211038065
       
  • Probabilistic cracking prediction via deep learned electrical tomography

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      Authors: Liang Chen, Adrien Gallet, Shan-Shan Huang, Dong Liu, Danny Smyl
      Abstract: Structural Health Monitoring, Ahead of Print.
      In recent years, electrical tomography, namely, electrical resistance tomography (ERT), has emerged as a viable approach to detecting, localizing and reconstructing structural cracking patterns in concrete structures. High-fidelity ERT reconstructions, however, often require computationally expensive optimization regimes and complex constraining and regularization schemes, which impedes pragmatic implementation in Structural Health Monitoring frameworks. To address this challenge, this article proposes the use of predictive deep neural networks to directly and rapidly solve an analogous ERT inverse problem. Specifically, the use of cross-entropy loss is used in optimizing networks forming a nonlinear mapping from ERT voltage measurements to binary probabilistic spatial crack distributions (cracked/not cracked). In this effort, artificial neural networks and convolutional neural networks are first trained using simulated electrical data. Following, the feasibility of the predictive networks is tested and affirmed using experimental and simulated data considering flexural and shear cracking patterns observed from reinforced concrete elements.
      Citation: Structural Health Monitoring
      PubDate: 2021-08-11T05:40:44Z
      DOI: 10.1177/14759217211037236
       
  • A three-stage online anomaly identification model for monitoring data in
           dams

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      Authors: Ying Xu, Huibao Huang, Yanling Li, Jingren Zhou, Xiang Lu, Yongfei Wang
      Abstract: Structural Health Monitoring, Ahead of Print.
      The monitoring of data anomaly identification is an important basis for dam safety online monitoring and evaluation. In this research, a cluster of anomaly identification models for dam safety monitoring data was constructed, and a three-stage online anomaly identification method was proposed to discriminate outliers. The proposed method combined anomaly detection for measured values based on a single-point time series simulation, measurement error reduction based on remote retesting and spatio-temporal analysis, and environmental response mutation recognition. It brought about efficient and accurate detection for data mutation and online classified identification for its inducement. Additionally, problems such as missing outliers, misjudging normal values induced by the environmental response, and difficulty in online identification for measurement errors were effectively solved. The research productions were applied to the online monitoring system for the safety risk of reservoirs and dams in the Dadu River Basin. The results showed that the proposed method could effectively improve the accuracy of anomaly identification and reduce the misjudgment and omission rate to less than 2%. It could also successfully recognize and subtract nonstructural anomalies such as accidental errors, instrument faults, and environmental responses online, which provided reliable data for online dam safety monitoring.
      Citation: Structural Health Monitoring
      PubDate: 2021-08-11T02:06:18Z
      DOI: 10.1177/14759217211025766
       
  • Novel tensor subspace system identification algorithm to identify
           time-varying modal parameters of bridge structures

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      Authors: Erhua Zhang, Di Wu, Deshan Shan
      Abstract: Structural Health Monitoring, Ahead of Print.
      Subspace-based system identification algorithms have been developed as an advanced technique for performing modal analysis. We introduce a novel tensor subspace-based algorithm to identify the time-varying modal parameters of bridge structures. A new time dimension is introduced in the traditional Hankel matrix, and a mathematical model of tensor subspace decomposition is established. Combined with the stabilization diagram, tensor parallel factor decomposition is used to estimate the frequencies, mode shapes, and modal damping ratios. The effectiveness of the proposed algorithm is validated by comparing it with the classical sliding-window–based stochastic subspace algorithm on a model cable-stayed bridge dynamic test. The proposed algorithm is further applied to process the dynamic responses of a real bridge health monitoring system to identify its time-varying modal frequencies. Our results demonstrated that the proposed algorithm significantly reduces computational efforts and extends the range of solution ideas for future out-only time-varying system identification problems.
      Citation: Structural Health Monitoring
      PubDate: 2021-08-10T01:29:44Z
      DOI: 10.1177/14759217211036024
       
  • Comparative study on the use of acoustic emission and vibration analyses
           for the bearing fault diagnosis of high-speed trains

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      Authors: Dongming Hou, Hongyuan Qi, Honglin Luo, Cuiping Wang, Jiangtian Yang
      Abstract: Structural Health Monitoring, Ahead of Print.
      A wheel set bearing is an important supporting component of a high-speed train. Its quality and performance directly determine the overall safety of the train. Therefore, monitoring a wheel set bearing’s conditions for an early fault diagnosis is vital to ensure the safe operation of high-speed trains. However, the collected signals are often contaminated by environmental noise, transmission path, and signal attenuation because of the complexity of high-speed train systems and poor operation conditions, making it difficult to extract the early fault features of the wheel set bearing accurately. Vibration monitoring is most widely used for bearing fault diagnosis, with the acoustic emission (AE) technology emerging as a powerful tool. This article reports a comparison between vibration and AE technology in terms of their applicability for diagnosing naturally degraded wheel set bearings. In addition, a novel fault diagnosis method based on the optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) and chirp Z-transform (CZT) is proposed to diagnose early composite fault defects in a wheel set bearing. The optimization CYCBD is adopted to enhance the fault-induced impact response and eliminate the interference of environmental noise, transmission path, and signal attenuation. CZT is used to improve the frequency resolution and match the fault features accurately under a limited data length condition. Moreover, the efficiency of the proposed method is verified by the simulated bearing signal and the real datasets. The results show that the proposed method is effective in the detection of wheel set bearing faults compared with the minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) methods. This research is also the first to compare the effectiveness of applying AE and vibration technologies to diagnose a naturally degraded high-speed train bearing, particularly close to actual line operation conditions.
      Citation: Structural Health Monitoring
      PubDate: 2021-08-06T01:26:39Z
      DOI: 10.1177/14759217211036025
       
  • Long-term guided wave structural health monitoring in an uncontrolled
           environment through long short-term principal component analysis

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      Authors: Kang Yang, Sungwon Kim, Rongting Yue, Haotian Yue, Joel B. Harley
      Abstract: Structural Health Monitoring, Ahead of Print.
      Environmental effects are a significant challenge in guided wave structural health monitoring systems. These effects distort signals and increase the likelihood of false alarms. Many research papers have studied mitigation strategies for common variations in guided wave datasets reproducible in a lab, such as temperature and stress. There are fewer studies and strategies for detecting damage under more unpredictable outdoor conditions. This article proposes a long short-term principal component analysis reconstruction method to detect synthetic damage under highly variational environments, like precipitation, freeze, and other conditions. The method does not require any temperature or other compensation methods and is tested by approximately seven million guided wave measurements collected over 2 years. Results show that our method achieves an area under curve score of near 0.95 when detecting synthetic damage under highly variable environmental conditions.
      Citation: Structural Health Monitoring
      PubDate: 2021-08-04T03:29:10Z
      DOI: 10.1177/14759217211035532
       
  • On the selection of the most plausible non-linear axial stress–strain
           model for railway ballast under different impulse magnitudes

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      Authors: Mujib Olamide Adeagbo, Heung-Fai Lam, Qin Hu
      Abstract: Structural Health Monitoring, Ahead of Print.
      The effective maintenance and health monitoring of ballasted railway tracks, which involves the determination of differential settlement, track support stress and stiffness, and the strain-hardening property of ballast, is essential. The vertical stress–strain behavior of the ballast layer is primarily responsible for the irrecoverable strains and settlements in tracks, leading to further track degradation. This article reports the development of a series of applicable yet simple uniaxial models and the selection of the most plausible one for capturing the behavior of vertical stresses and strains in ballasts utilizing a set of measured vibration data of the rail–sleeper–ballast system from a Bayesian perspective. From the literature, the dynamic behavior of ballast can be divided into linear and non-linear regions. Under small amplitude vibration, the stress–strain property is linear and elastic, while the behavior becomes non-linear and inelastic once the elasticity limit is exceeded. By integrating the linear phase to some well-known non-linear engineering material laws, a list of new ballast stress–strain model classes was developed. An enhanced Markov chain Monte Carlo–based Bayesian scheme was utilized to explicitly handle the uncertainties in the model updating process, while the Bayesian model class selection method was employed to select the most plausible ballast stress–strain model class under the prevailing system conditions. The proposed methodology was verified using three sets of measured acceleration data from impact hammer tests on an in situ sleeper with simulated ballast damage. The obtained results suggest that the linear-elastic model is sufficient for small amplitude vibrations, while the modified Voce model is the most plausible amongst the investigated model classes for high impact load. The results also demonstrate the importance of the non-linear ballast model in ballast damage identification and the potential applicability of the selected ballast model in field track monitoring.
      Citation: Structural Health Monitoring
      PubDate: 2021-07-30T03:57:18Z
      DOI: 10.1177/14759217211033968
       
  • Ultrasonic monitoring of stress and cracks of the 1/3 scale mock-up of
           nuclear reactor concrete containment structure

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      Authors: Qi Xue, Eric Larose, Ludovic Moreau, Romain Thery, Odile Abraham, Jean-Marie Henault
      Abstract: Structural Health Monitoring, Ahead of Print.
      To evaluate the stress level and damage of a reinforced concrete containment wall (similar to those used in nuclear power plants) and its reaction to pressure variations, we conducted successive ultrasonic experiments on the exterior surface of the containment wall in the gusset area for three consecutive years (2017, 2018 and 2019). During each experiment, the pressure inside the containment wall increased gradually from 0 MPa to 0.43 MPa and then decreased back to 0 MPa. From the analysis of the ultrasonic coda waves obtained in the multiple scattering regime (80–220 kHz), we performed Coda Wave Interferometry to calculate the apparent velocity changes in the structure (denoted by dV/Va) and Coda Wave Decorrelation (DC) measurements to produce 3D cartographies of stress and crack distribution. From three source–receiver pairs, located at the top, middle and bottom of the experimental region, we observe that coda waves dilate, shrink and remain almost unchanged, respectively. This corresponds to the decreasing, increasing and invariant pressure inside the concrete. The comparison of 3 years’ results demonstrates that the variation of dV/Va and DC under the same pressure test increases through the years, which indicates the progressive deterioration and ageing of the concrete. From a large collection of source–receiver pairs at different times, the spatial–temporal variations of dV/Va and DC are then used to produce a map of the structural velocity and scattering changes, respectively. We observe a decreasing velocity on the top part and an increasing in the middle one, which is in line with the dV/Va analysis. The reconstructed scattering changes (or structural changes) highlight the active region during the inflation–deflation procedure, corresponding to the opening and closing (and sometimes the development) of cracks. The larger magnitude in 2019 than in 2017 indicates the increasing damage in the concrete.
      Citation: Structural Health Monitoring
      PubDate: 2021-07-29T01:56:51Z
      DOI: 10.1177/14759217211034729
       
  • Acoustic emission and moving window-improved kernel entropy component
           analysis for structural condition monitoring of hoisting machinery under
           various working conditions

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      Authors: Yang Li, Feiyun Xu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Acoustic emission (AE) has been widely used to the nondestructive evaluation (NDE) and structural health monitoring (SHM) of hoisting machinery recently. Kernel entropy component analysis (KECA) is generally applied to extract the AE features based on its excellent nonlinear ability. However, traditional KECA specifically requires a considerable number of components (e.g. eigenvalues and eigenvectors) to excellently describe the original data, which leads to a reduction in the effect of approximate dimensionality reduction of high-dimensional data, thus causing readily unacceptable condition monitoring result. To overcome this weakness, a novel method named moving window-improved kernel entropy component analysis (MW-IKECA) is proposed in this study for structural condition monitoring of hoisting machinery, which is aimed at extracting more AE feature information and improving the condition identification accuracy. Firstly, a twiddle factor is introduced in the KECA model for the purpose of breaking the restriction that the projection axes originate only from the feature vectors and maximizing the independence between the components. Meanwhile, the moving window local strategy is incorporated into the proposed IKECA to extract more rich and effectiveness AE feature information at different scales. Finally, the Cauchy–Schwarz (CS) statistic is utilized to calculate the similarity between probability density functions and maintain the angular structure of the MW-IKECA feature space for the task of improving the monitoring accuracy and shortening the monitoring time-delay of MW-IKECA. Results of the experimental and practical engineering application validate the effectiveness and superiority of the proposed method in AE-based crane SHM under different working conditions compared with the traditional KECA and some combinatorial methods.
      Citation: Structural Health Monitoring
      PubDate: 2021-07-27T12:24:46Z
      DOI: 10.1177/14759217211033627
       
  • A simplified framework for prediction of sensor network coverage in
           real-time structural health monitoring of plate-like structures

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      Authors: Mehrdad Ghyabi, Hamidreza Nemati, Ehsan Dehghan-Niri
      Abstract: Structural Health Monitoring, Ahead of Print.
      In this article, the coverage area prediction of piezoelectric sensor network for detecting a specific type of under-surface crack in plate-like structures is addressed. In particular, this article proposes a simplified framework to estimate the coverage of any given sensor network arrangement when a critical defect is known. Based on numerical results from finite element methods (FEM), a simplified framework to estimate coverage area of any given network arrangement is developed. Using such a simplified framework, one can avoid time-consuming procedure of evaluating numerous FEM models in estimating sensor network coverage. Back-scatter fields of partial cracks are estimated using a proposed function, whose parameters are estimated from the results of a limited number of FEM simulations. The proposed function efficiently predicts the back-scattered field of any combination of transmitters and receivers for a given crack geometry. Superposition is used to estimate the coverage area of an arbitrary piezoelectric (e.g., PZT) sensor network. It is shown that the coverage area of a sensor network depends on both sensor network geometry and defect properties (e.g., crack inclination) and it is not necessarily a linear function of the number of sensors. Furthermore, it is shown that the network arrangement has an important effect on the geometry of the coverage area. Experimental results of a network of 14 PZTs in two clusters confirm the accuracy of the method.
      Citation: Structural Health Monitoring
      PubDate: 2021-07-20T05:02:17Z
      DOI: 10.1177/14759217211033217
       
  • Probability-based diagnostic imaging with corrected weight distribution
           for damage detection of stiffened composite panel

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      Authors: Guoqiang Liu, Binwen Wang, Li Wang, Yu Yang, Xiaguang Wang
      Abstract: Structural Health Monitoring, Ahead of Print.
      Due to no requirement for direct interpretation of the guided wave signal, probability-based diagnostic imaging (PDI) algorithm is especially suitable for damage identification of complex composite structures. However, the weight distribution function of PDI algorithm is relatively inaccurate. It can reduce the damage localization accuracy. In order to improve the damage localization accuracy, an improved PDI algorithm is proposed. In the proposed algorithm, the weight distribution function is corrected by the acquired relative distances from defects to all actuator–sensor pairs and the reduction of the weight distribution areas. The validity of the proposed algorithm is assessed by identifying damages at different locations on a stiffened composite panel. The results show that the proposed algorithm can identify damage of a stiffened composite panel accurately.
      Citation: Structural Health Monitoring
      PubDate: 2021-07-20T04:48:31Z
      DOI: 10.1177/14759217211033967
       
  • Inspection interval optimization of aircraft landing gear component based
           

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      Authors: Youngjun Lee, Jongwoon Park, Dooyoul Lee
      Abstract: Structural Health Monitoring, Ahead of Print.
      The nondestructive inspection interval is highly related with both system reliability and maintenance burden. Conventional inspection interval decision criteria based on the deterministic crack propagation analysis could require too much frequent inspection or sometimes occur structural failure owing to the rapid crack propagation than expected. The stochastic crack growth analysis method was proposed to compensate for the shortcomings of the deterministic analysis. This research studied the crack growth of aircraft landing gear components based on the equivalent initial flaw size distribution algorithm, and then we assessed failure risk. The calculated risk was validated using Monte-Carlo simulation, and finally, the optimum inspection interval was proposed to satisfy the US Airforce risk management criteria.
      Citation: Structural Health Monitoring
      PubDate: 2021-07-19T01:43:34Z
      DOI: 10.1177/14759217211033625
       
  • Brillouin scattering spectrum-based crack measurement using distributed
           fiber optic sensing

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      Authors: Ruonan Ou, Linqing Luo, Kenichi Soga
      Abstract: Structural Health Monitoring, Ahead of Print.
      Brillouin scattering-based distributed fiber optic sensing (Brillouin-DFOS) technology is widely used in health monitoring of large-scale structures with the aim to provide early warning of structural degradation and timely maintenance and renewal. Material cracking is one of the key mechanisms that contribute to structural failure. However, the conventional strain measurement using the Brillouin-DFOS system has a decimeter-order spatial resolution, and therefore it is difficult to measure the highly localized strain generated by a sub-millimeter crack. In this study, a new crack analysis method based on Brillouin scattering spectrum (BSS) data is proposed to overcome this spatial resolution-induced measurement limitation. By taking the derivative of the BSS data and tracking their local minimums, the method can extract the maximum strain within the spatial resolution around the measurement points. By comparing the variation of the maximum strain within the spatial resolution around different measurement points along the fiber, cracks can be located. The performance of the method is demonstrated and verified by locating and quantifying a small gap created between two wood boards when one of the wood boards is pushed away from the other. The test result verifies the accuracy of the crack strain quantification of the method and proves its capability to measure a sub-millimeter crack. The method is also applied to a thin bonded concrete overlay of asphalt pavement field experiment, in which the growth of a transverse joint penetrating through the concrete–asphalt interface was monitored. The method successfully locates the position, traces the strain variation, and estimates the width of a crack less than [math] wide using a Brillouin-DFOS system with [math] spatial resolution.
      Citation: Structural Health Monitoring
      PubDate: 2021-07-15T06:19:16Z
      DOI: 10.1177/14759217211030913
       
  • Techniques of corrosion monitoring of steel rebar in reinforced concrete
           structures: A review

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      Authors: Liang Fan, Xianming Shi
      Abstract: Structural Health Monitoring, Ahead of Print.
      Reinforcement corrosion is a major culprit that undermines the service life of reinforced concrete (RC) structures. It costs billions of dollars each year in the US alone and negatively impacts safety, reliability, resilience, and environmental performance of RC infrastructure. Corrosion monitoring of steel rebar is critical to provide early warning of deficiency, enable timely and effective maintenance strategies, and prevent catastrophes associated with premature failure of RC structures. To improve corrosion monitoring in the engineering practice, this work comprehensively reviews the state of the art of five major types of techniques, that is, electrochemical sensors, optical fiber sensors, sensors based on elastic wave methods, sensing based on electromagnetic methods, and untouched sensors. Each type of technique is systematically divided into sub-categories based on the sensing principle and key characteristics. For each sensor type, its maturity, application range, and the problems that hinder its application are discussed, aimed to provide guidance in selecting the appropriate sensors for specific engineering applications. This work concludes with an overview of opportunities and challenges, including the following six aspects for future research: spatial resolution, sensitivity, and measurement range; reliability and interpretation of the measurement; sensor selection based on three corrosion stages of steel rebar; long-term stability and service life; cost-efficiency of the sensing system; and monitoring of existing structure.
      Citation: Structural Health Monitoring
      PubDate: 2021-07-15T04:06:42Z
      DOI: 10.1177/14759217211030911
       
  • Sparse wavenumber analysis of guided wave based on hybrid Lasso regression
           in composite laminates

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      Authors: Yue Hu, Fangsen Cui, Fucai Li, Xiaotong Tu, Liang Zeng
      Abstract: Structural Health Monitoring, Ahead of Print.
      The guided wave is an efficient and reliable tool for the structural health monitoring (SHM) of the composite laminates. In the guided wave-based SHM methods, extracting the dispersion curves is essential for integrity evaluation. In this study, a sparse wavenumber analysis based on hybrid least absolute shrinkage and selection operator (Lasso) regression is proposed to extract the dispersion curves in the frequency–wavenumber distribution (FKD) for the composite laminate. The hybrid Lasso regression model is constructed based on the guided wave propagation mechanism. Considering that responses of some wave modes are very weak at specific frequencies due to the guided wave attenuation in the composite laminates, the group-sparsity and continuity regularizations are imposed in this model to improve frequency–wavenumber resolution and remove noises. Only few sensors are required for the proposed method to extract the dispersion curves. Both the simulation and the experiment are used to verify the effectiveness of the proposed method. Furthermore, the material property of the composite laminate in the experiment is non-destructively estimated by using the dispersion curves extracted by the proposed method.
      Citation: Structural Health Monitoring
      PubDate: 2021-07-14T10:45:15Z
      DOI: 10.1177/14759217211032118
       
  • Value of information of structural health monitoring with temporally
           dependent observations

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      Authors: Jannie S Nielsen
      Abstract: Structural Health Monitoring, Ahead of Print.
      A Bayesian approach is often applied when updating a deterioration model using observations from inspections, structural health monitoring, or condition monitoring. The observations are stochastic variables with probability distributions that depend on the damage size. Consecutive observations are usually assumed to be independent of each other, but this assumption does not always hold, especially when using online monitoring systems. Frequent updating using dependent measurements can lead to an over-optimistic assessment of the value of information when the measurements are incorrectly modeled as being independent. This article presents a Bayesian network modeling approach for the inclusion of temporal dependency between measurements through a dependency parameter and presents a generic monitoring model based on the exceedance of thresholds for a damage index. Additionally, the model is implemented in a computational framework for risk-based maintenance planning, developed for maintenance planning for wind turbines. The framework is applied for a numerical experiment, where the expected lifetime costs are found for strategies with monitoring with and without dependency between observations, and also for the case where dependency is present but is neglected when making decisions. The numerical experiment and associated parameter study show that neglecting dependency in the decision model when the observations are in fact dependent can lead to much higher costs than expected and to the selection of non-optimal strategies. Much lower costs (down to one quarter) can be obtained when the dependency is properly modeled. In the case of temporally dependent observations, an advanced decision model using a Bayesian network as a simple digital twin is needed to make monitoring feasible compared to only using inspections.
      Citation: Structural Health Monitoring
      PubDate: 2021-07-13T10:42:45Z
      DOI: 10.1177/14759217211030605
       
  • A general framework for supervised structural health monitoring and sensor
           output validation mitigating data imbalance with generative adversarial
           networks-generated high-dimensional features

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      Authors: Mohammad Hesam Soleimani-Babakamali, Roksana Soleimani-Babakamali, Rodrigo Sarlo
      Abstract: Structural Health Monitoring, Ahead of Print.
      This study proposes a novelty-classification framework that applies to structural health monitoring (SHM) and sensor output validation (SOV) problems. The proposed framework has simple high-dimensional features with several advantages. First, the feature extraction method is extensively applicable to instrumented structures. Second, the high-dimensional features’ utilization alleviates one of the main issues of supervised novelty classifications, namely, imbalanced datasets and low-sampled data classes. Recurrent Neural Networks are employed for the classification of high-dimensional features. Furthermore, generative adversarial networks (GAN) are trained with low-sampled data classes’ high-dimensional features for generating new data objects. The generated data objects are combined with the initial training set for improving classification results. The proposed framework is studied on two SHM and SOV datasets. The SHM dataset has twenty-one data classes, with a total test accuracy of 99.60% compared to another study with 88.13% accuracy. The SOV classification shows improved results with a mean accuracy of 96.5% compared to three other studies with mean accuracy values of 93.5%, 92.97%, and 71.1%. Furthermore, the integration of GAN’s generated data objects with low-sampled classes improved those classes’ mean F1 score from 44.77% to 64.58% and from 73.39% to 90.84% on SOV and SHM case studies, respectively. The integration of GAN-generated data objects with the initial low-sampled data classes for accuracy improvement shows more potential in the SHM dataset than the SOV case, which can be due to the signal pattern-based labeling logic of SOV datasets.
      Citation: Structural Health Monitoring
      PubDate: 2021-07-13T02:50:08Z
      DOI: 10.1177/14759217211025488
       
  • An alternative quantification of the value of information in structural
           health monitoring

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      Authors: Mayank Chadha, Zhen Hu, Michael D Todd
      Abstract: Structural Health Monitoring, Ahead of Print.
      Analogous to an experiment, a structural health monitoring (SHM) system may be thought of as an information-gathering mechanism. Gathering the information that is representative of the structural state and correctly inferring its meaning helps engineers (decision-makers) mitigate possible losses by taking appropriate actions (risk-informed decision-making). However, the design, research, development, installation, maintenance, and operation of an SHM system are an expensive endeavor. Therefore, the decision to invest in new information is rationally justified if the reduction in the expected losses by utilizing newly acquired information is more than the intrinsic cost of the information acquiring mechanism incurred over the lifespan of the structure. This article investigates the economic advantage of installing an SHM system for inference of the structural state, risk, and lifecycle management by using the value of information (VoI) analysis. Among many possible choices of SHM system designs (different information-gathering mechanisms), pre-posterior decision analysis can be used to select the most feasible design. Traditionally, the cost–benefit analysis of an SHM system is carried out through pre-posterior decision analysis that helps one evaluate the benefit of an experiment or an information-gathering mechanism using the expected value of information metric. This study proposes an alternate normalized metric that evaluates the expected reward ratio (benefit/gain of using an SHM system) relative to the investment risk (cost of SHM over the lifecycle). The analysis of evaluating the relative benefit of various SHM system designs is carried out by considering the concept of the VoI, by performing pre-posterior analysis, and the idea of a perfect experiment is discussed.
      Citation: Structural Health Monitoring
      PubDate: 2021-07-08T03:44:39Z
      DOI: 10.1177/14759217211028439
       
  • Data-driven dictionary design–based sparse classification method for
           intelligent fault diagnosis of planet bearings

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      Authors: Yun Kong, Zhaoye Qin, Tianyang Wang, Meng Rao, Zhipeng Feng, Fulei Chu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Planet bearings have remained as the challenging components for health monitoring and diagnostics in the planetary transmission systems of helicopters and wind turbines, due to their intricate kinematic mechanisms, strong modulations, and heavy interferences from gear vibrations. To address intelligent diagnostics of planet bearings, this article presents a data-driven dictionary design–based sparse classification (DDD-SC) approach. DDD-SC is free of detecting the weak frequency features and can achieve reliable fault recognition performances for planet bearings without establishing any explicit classifiers. In the first step, DDD-SC implements the data-driven dictionary design with an overlapping segmentation strategy, which leverages the self-similarity features of planet bearing data and constructs the category-specific dictionaries with strong representation power. In the second step, DDD-SC implements the sparsity-based intelligent diagnosis with the sparse representation–based classification criterion and differentiates various planet bearing health states based on minimal sparse reconstruction errors. The effectiveness and superiority of DDD-SC for intelligent planet bearing fault diagnosis have been demonstrated with an experimental planetary transmission system. The extensive diagnosis results show that DDD-SC can achieve the highest diagnosis accuracy, strongest anti-noise performance, and lowest computation costs in comparison with three classical sparse representation–based classification and two advanced deep learning methods.
      Citation: Structural Health Monitoring
      PubDate: 2021-07-02T05:01:32Z
      DOI: 10.1177/14759217211029016
       
  • Data privacy preserving federated transfer learning in machinery fault
           diagnostics using prior distributions

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      Authors: Wei Zhang, Xiang Li
      Abstract: Structural Health Monitoring, Ahead of Print.
      Federated learning has been receiving increasing attention in the recent years, which improves model performance with data privacy among different clients. The intelligent fault diagnostic problems can be largely benefited from this emerging technology since the private data generally cannot leave local storage in the real industries. While promising federated learning performance has been achieved in the literature, most studies assume data from different clients are independent and identically distributed. In the real industrial scenarios, due to variations in machines and operating conditions, the data distributions are generally different across different clients, that significantly deteriorates the performance of federated learning. To address this issue, a federated transfer learning method is proposed in this article for machinery fault diagnostics. Under the condition that data from different clients cannot be communicated, prior distributions are proposed to indirectly bridge the domain gap. In this way, client-invariant features can be extracted for diagnostics while the data privacy is preserved. Experiments on two rotating machinery datasets are implemented for validation, and the results suggest the proposed method offers an effective and promising approach for federated transfer learning in fault diagnostic problems.
      Citation: Structural Health Monitoring
      PubDate: 2021-07-01T01:26:09Z
      DOI: 10.1177/14759217211029201
       
  • An innovative deep neural network–based approach for internal cavity
           detection of timber columns using percussion sound

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      Authors: Lin Chen, Haibei Xiong, Xiaohan Sang, Cheng Yuan, Xiuquan Li, Qingzhao Kong
      Abstract: Structural Health Monitoring, Ahead of Print.
      Timber structures have been a dominant form of construction throughout most of history and continued to serve as a widely used staple of civil infrastructure in the modern era. As a natural material, wood is prone to termite damages, which often cause internal cavities for timber structures. Since internal cavities are invisible and greatly weaken structural load-bearing capacity, an effective method to timber internal cavity detection is of great importance to ensure structural safety. This article proposes an innovative deep neural network (DNN)–based approach for internal cavity detection of timber columns using percussion sound. The influence mechanism of percussion sound with the volume change of internal cavity was studied through theoretical and numerical analysis. A series of percussion tests on timber column specimens with different cavity volumes and environmental variations were conducted to validate the feasibility of the proposed DNN-based approach. Experimental results show high accuracy and generality for cavity severity identification regardless of percussion location, column section shape, and environmental effects, implying great potentials of the proposed approach as a fast tool for determining internal cavity of timber structures in field applications.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-23T08:14:54Z
      DOI: 10.1177/14759217211028524
       
  • A method for the experimental estimation of direct and cross-coupled
           dynamic coefficients of tilting-pad journal bearings of vertical
           hydro-generators

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      Authors: Geraldo C Brito, Roberto D Machado, Anselmo C Neto, Leonardo Y Kimura
      Abstract: Structural Health Monitoring, Ahead of Print.
      This article presents a method to experimentally estimate the direct and cross-coupled dynamic coefficients of tilting-pad journal bearings of vertical hydro-generators and other similar rotating machinery for damage detection purposes. Based on a simplified second-order model of a journal bearing in the state-space, the method employs only the usually monitored vibrations, the shaft radial relative, and the bearing radial absolute vibrations originated by the hydro-generator residual unbalance or by hydraulic excitations in the turbine rotor. This article shows that the method was successfully tested using the shaft and bearing vibration signals synthesized by a mathematical model of a 700 MW hydro-generator, even when these signals are contaminated with random noise. This article also shows the method’s performance when applied to real vibration signals acquired from the modeled hydro-generator. Finally, it discusses the possible measures to improve the method’s efficiency.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-22T10:25:45Z
      DOI: 10.1177/14759217211026593
       
  • Statistical guided-waves-based structural health monitoring via stochastic
           non-parametric time series models

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      Authors: Ahmad Amer, Fotis P Kopsaftopoulos
      Abstract: Structural Health Monitoring, Ahead of Print.
      Damage detection in active-sensing, guided-waves-based structural health monitoring (SHM) has evolved through multiple eras of development during the past decades. Nevertheless, there still exist a number of challenges facing the current state-of-the-art approaches, both in the industry as well as in research and development, including low damage sensitivity, lack of robustness to uncertainties, need for user-defined thresholds, and non-uniform response across a sensor network. In this work, a novel statistical framework is proposed for active-sensing SHM based on the use of ultrasonic guided waves. This framework is based on stochastic non-parametric time series models and their corresponding statistical properties in order to readily provide healthy confidence bounds and enable accurate and robust damage detection via the use of appropriate statistical decision-making tests. Three such methods and corresponding statistical quantities (test statistics) along with decision-making schemes are formulated and experimentally assessed via the use of three coupons with different levels of complexity: an Al plate with a growing notch, a carbon fiber-reinforced plastic (CFRP) plate with added weights to simulate local damage, and the CFRP panel used in the Open Guided Waves project, all fitted with piezoelectric transducers under a pitch-catch configuration. The performance of the proposed methods is compared to that of state-of-the-art time-domain damage indices (DIs). The results demonstrate the increased detection sensitivity and robustness of the proposed methods, with better tracking capability of damage evolution compared to conventional approaches, even for damage-non-intersecting actuator–sensor paths. In particular, the Z statistic emerges as the best damage detection metric compared to conventional DIs, as well as the other proposed statistics. Overall, the proposed statistics in this study promise greater damage sensitivity across different components, with enhanced robustness to uncertainties, as well as user-friendly application.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-19T08:20:31Z
      DOI: 10.1177/14759217211024527
       
  • All-phase fast Fourier transform and multiple cross-correlation analysis
           based on Geiger iteration for acoustic emission sources localization in
           complex metallic structures

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      Authors: Yang Li, Feiyun Xu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Nowadays, the localization and identification of acoustic emission (AE) source is widely utilized to structural health monitoring (SHM) of complex metallic structures. However, traditional AE source localization methods are generally difficult to localize and characterize AE sources in plate-like structure that has complex geometric features. To alleviate the problem, a novel AE source localization method based on all-phase fast Fourier transform and multiple cross-correlation analysis is proposed in this article. Moreover, least squares and Geiger iteration algorithm are applied to determine the coordinates of AE sources. In addition, an improved Bayesian information criterion (BIC) version named autoregressive BIC (i.e., AR-BIC) is presented to increase the accuracy of source localization. To validate the performance of the proposed approach, the classical pencil lead break tests are carried out on a 316 L stainless steel with 10 laser cladding layers. Experimental waveforms are generated from AE sources near laser cladding layers, the surface of the structure, and on its edges. Additionally, to evaluate the performance of the proposed approach in three-dimensional AE source localization, an industrial storage tank is used to acquire three-dimensional AE sources through manually striking. Finally, to further verify the effectiveness of the proposed approach, comparisons with conventional AE source location methods (i.e., PAC or SAMOS AE acquisition system, Newton’s method, and multiple cross-correlation based on Geiger algorithm) and two representative approaches (i.e., deep learning and Bayesian methodology) for localizing AE sources generated by complex metallic structures are conducted. The comparative results demonstrate the effectiveness and superiority of the proposed method in AE-based SHM of complex metallic structures.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-19T08:13:30Z
      DOI: 10.1177/14759217211027481
       
  • Quantitative acoustic emission investigation on the crack evolution in
           concrete prisms by frequency analysis based on wavelet packet transform

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      Authors: Zonglian Wang, Keqin Ding, Huilan Ren, Jianguo Ning
      Abstract: Structural Health Monitoring, Ahead of Print.
      To gain an insight into the evolution of micro-cracks in concrete materials, a quantitative acoustic emission investigation on the damage process of concrete prisms subjected to three-point bending loading was performed. Each of the monitored acoustic emission signals was processed by a two-level wavelet packet decomposition into four different frequency bands (AA2, DA2, AD2, and DD2), and the energy coefficients R1, R2, R3, and R4 that parameterize their characteristic frequency bands were calculated. By analyzing variations in energy coefficients of the lowest frequency band (AA2), R1, and the energy coefficients of the highest frequency band (DD2), R4, the whole damage process was divided into three stages: crack initiation, crack growth, and crack coalescence. An inverse relationship between the frequency of the acoustic emission signal emitted by the propagating crack and the crack size in concrete materials was acquired based on the damage theory of brittle materials and the strain energy release theory. The statistical analysis results of the experimental data indicated that the average of R1 increased in turn, and the average of R4 correspondingly decreased in turn from Stage 1 to Stage 3. It revealed that the frequencies of acoustic emission signals decreased gradually with the evolution of the damage of concrete prisms, which is in a good agreement with the theoretical analysis result.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-17T10:30:08Z
      DOI: 10.1177/14759217211018871
       
  • Uncertainty quantification for impact location and force estimation in
           composite structures

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      Authors: Aldyandra Hami Seno, MH Ferri Aliabadi
      Abstract: Structural Health Monitoring, Ahead of Print.
      Structural health monitoring of impact location and severity using Lamb waves has been proven to be a reliable method under laboratory conditions. However, real-life operational and environmental conditions (vibration noise, temperature changes, different impact scenarios, etc.) and measurement errors are known to generate variation in Lamb wave features which may significantly affect the accuracy of these estimates. Therefore, these uncertainties should be considered, as a deterministic approach may lead to erroneous decisions. In this article, a novel data-driven stochastic Kriging-based method for impact location and maximum force estimation, that is able to reliably quantify the output uncertainty is presented. The method utilises a novel modification of the kriging technique (normally used for spatial interpolation of geostatistical data) for statistical pattern matching and uncertainty quantification using Lamb wave features to estimate the location and maximum force of impacts. The data was experimentally obtained from a composite panel equipped with piezoelectric sensors. Comparison with a deterministic benchmark method developed in prior studies shows that the proposed method gives a more reliable estimate for experimental impacts under various simulated environmental and operational conditions by estimating the uncertainty. The developed method highlights the suitability of data-driven methods for uncertainty quantification, by taking advantage of the relationship between data points in the reference database that is a mandatory component of these methods (and is often seen as a disadvantage). By quantifying the uncertainty, there is more information for operators to reliably locate impacts and estimate the severity, leading to robust maintenance decisions.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-17T10:29:46Z
      DOI: 10.1177/14759217211020255
       
  • Defect detection on the curved surface of a wind turbine blade using
           piezoelectric flexible line sensors

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      Authors: Sang-Hyeon Kang, Myeongcheol Kang, Lae-Hyong Kang
      Abstract: Structural Health Monitoring, Ahead of Print.
      Blades play a critical role in the wind turbine system. Therefore, their structural health monitoring is very important. Blades are damaged by sudden changes in wind load, cracks due to collision of foreign objects, and disasters, such as lightning strikes, hail, and typhoons. Moreover, blades are expensive to maintain. Defects or damages to wind turbine blades reduce the life span and power generation efficiency of the wind turbine and increase safety risks and maintenance costs. Therefore, it is very important to detect blade damage to prevent problems in the wind turbine. Ultrasonic inspection is suitable for blades made of composite materials. Piezoelectric ceramic, which is a typical piezoelectric element, has relatively high sensitivity compared to other sensors. However, it suffers from brittle fractures and thus difficult to apply to curved structures. To overcome the limitations of piezoelectric ceramics, a piezoelectric flexible line sensor that can be applied to curved surfaces was manufactured using the dice-and-fill method for a [Pb(Li0.25Nb0.75)]0.06 [Pb(Mg0.33Nb0.67)]0.06 [Pb(Zr0.50Ti0.50)]0.88O3 with 0.7 wt% MnO2 (PZTNMML) ceramic disc. Instead of a typical ultrasonic inspection method with limited surface contact, a laser capable of producing ultrasonic excitation of ultrasonic waves over a large area from a long distance was used. The possibility of detecting a defect on the wind turbine blade using a piezoelectric flexible line sensor and laser ultrasound was confirmed in this study.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-17T07:39:47Z
      DOI: 10.1177/14759217211026192
       
  • Modulation signal bispectrum with optimized wavelet packet denoising for
           rolling bearing fault diagnosis

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      Authors: Junchao Guo, Zhanqun Shi, Dong Zhen, Zhaozong Meng, Fengshou Gu, Andrew D Ball
      Abstract: Structural Health Monitoring, Ahead of Print.
      Transient impulses caused by local faults are critical informative indicators for rolling element bearing fault diagnosis. The methods for accurately extracting transient impulses while suppressing strong background noise and interference components have received extensive studies. In this article, a novel fault diagnosis scheme based on optimized wavelet packet denoising and modulation signal bispectrum is proposed, which takes advantage of the transient impulse enhancement of wavelet packet denoising and the demodulation ability of modulation signal bispectrum to diagnose bearing faults more accurately. First, the measured signals are decomposed into a series of time–frequency subspaces using wavelet packet transform. An optimal threshold value is selected based on the proposed threshold criterion by considering unbiased autocorrelation of envelope and Gini index of the transient impulses. Subsequently, the subspaces are denoised by the wavelet packet denoising with the optimized threshold value, and the master subspaces that containing the fault-related transient impulses are selected based on the Gini index indicator. Finally, the modulation signal bispectrum is utilized to further purify the signal and extract the modulation components contained in the transient impulses, and the suboptimal modulation signal bispectrum slices are selected based on the characteristic frequency intensity coefficient. The modulation signal bispectrum detector is then obtained by averaging the suboptimal modulation signal bispectrum slices to determine the type of the bearing faults. The proposed wavelet packet denoising-modulation signal bispectrum is validated based on the simulation and experimental studies. Compared with the variational mode decomposition and Teager energy operator, fast kurtogram as well as conventional modulation signal bispectrum, the proposed wavelet packet denoising-modulation signal bispectrum method has superior performance in extracting the fault feature of the incipient defects on different bearing components.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-14T07:30:01Z
      DOI: 10.1177/14759217211018281
       
  • Damage imaging in skin-stringer composite aircraft panel by
           ultrasonic-guided waves using deep learning with convolutional neural
           network

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      Authors: Ranting Cui, Guillermo Azuara, Francesco Lanza di Scalea, Eduardo Barrera
      Abstract: Structural Health Monitoring, Ahead of Print.
      The detection and localization of structural damage in a stiffened skin-to-stringer composite panel typical of modern aircraft construction can be addressed by ultrasonic-guided wave transducer arrays. However, the geometrical and material complexities of this part make it quite difficult to utilize physics-based concepts of wave scattering. A data-driven deep learning (DL) approach based on the convolutional neural network (CNN) is used instead for this application. The DL technique automatically selects the most sensitive wave features based on the learned training data. In addition, the generalization abilities of the network allow for detection of damage that can be different from the training scenarios. This article describes a specific 1D-CNN algorithm that has been designed for this application, and it demonstrates its ability to image damage in key regions of the stiffened composite test panel, particularly the skin region, the stringer’s flange region, and the stringer’s cap region. Covering the stringer’s regions from guided wave transducers located solely on the skin is a particularly attractive feature of the proposed SHM approach for this kind of complex structure.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-12T11:20:47Z
      DOI: 10.1177/14759217211023934
       
  • Defect detection in guided wave signals using nonlinear autoregressive
           exogenous method

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      Authors: Kangwei Wang, Jie Zhang, Yi Shen, Benjamin Karkera, Anthony J Croxford, Paul D Wilcox
      Abstract: Structural Health Monitoring, Ahead of Print.
      To perform long-term structural health monitoring, a method based on a nonlinear autoregressive exogenous network is used to learn the features present in signals acquired from a pristine structure. When a subsequent measured signal is input to the trained nonlinear autoregressive exogenous network, the output is a prediction of the equivalent signal from a pristine structure. The residual when the pristine predicted signal is subtracted from the measured signal is used for defect detection and localization. A methodology of how to train, test and assess a nonlinear autoregressive exogenous network for guided wave signals is introduced and applied to experimental data obtained over a period of 8 years from a sparse array of guided wave sensors deployed on a steel storage tank. A separate nonlinear autoregressive exogenous model is trained for each sensor pair in the array using data captured in 2012. The method is first tested using data from a single pair of sensors. Defect signals are synthesized by superposing simulated responses from defects onto later experimental signals obtained from the real structure. The test results for the nonlinear autoregressive exogenous method show better detection performance than those from the optimal baseline selection method, in terms of receiver operating characteristic curves. The detection performance of the nonlinear autoregressive exogenous method is further assessed on signals from the whole sensor array, again with simulated defect responses superposed. It is shown that good detection and localization performance can be achieved by combining the nonlinear autoregressive exogenous residual signals from different sensor pairs. The nonlinear autoregressive exogenous method is tested on experimental data acquired at intervals over the following 7 years as the condition of the tank naturally degrades. Indications from localized corrosion are observed. Finally, an artificial localized anomaly is added to the tank and is visible at the correct location in the image formed using the nonlinear autoregressive exogenous method.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-12T05:42:09Z
      DOI: 10.1177/14759217211018698
       
  • Piezoelectric rod sensors for scour detection and vortex-induced vibration
           monitoring

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      Authors: Morgan L Funderburk, Yujin Park, Anton Netchaev, Kenneth J Loh
      Abstract: Structural Health Monitoring, Ahead of Print.
      As extreme events increase in frequency, flow-disrupting large-scale structures become ever more susceptible to collapse due to local scour effects. The objective of this study was to validate the functionality of passive, flow-excited scour sensors that can continue to operate during an extreme event. The scour sensors, or piezo-rods, feature continuous piezoelectric polymer strips embedded within and along the length of slender cylindrical rods, which could then be driven into the soil where scour is expected. When scour erodes away foundation material to reveal a portion of the piezo-rod, ambient fluid flow excitations would cause the piezoelectric element to output a voltage response corresponding to the dynamic bending strains of the sensor. The voltage response is dependent on both the structural dynamic properties of the sensor and the excitation fluid’s velocity. By monitoring both shedding frequency and flow velocity, the exposed length of the piezo-rod (or scour depth) can be calculated. Two series of experimental tests were conducted in this work: (1) the piezo-rod was driven into sediment around a mock pier to collect scour data, and (2) the piezo-rod was used to monitor its own structural response by collecting vortex-shedding frequency data in response to varied flow velocities to establish a velocity–frequency relationship. The results showed that the piezo-rod successfully captured structural vortex-shedding frequency comparable to state-of-practice testing. A one-dimensional numerical model was developed using the velocity–frequency relationship to increase the accuracy of voltage-based length prediction of the piezo-rod. Two-dimensional flow modeling was also performed for predicting localized velocities within a complex flow field. These velocities, in conjunction with the velocity–frequency relationship, were used to greatly improve length-predictive capabilities.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-09T12:22:07Z
      DOI: 10.1177/14759217211018821
       
  • Ultrasound tomography for health monitoring of carbon fibre–reinforced
           polymers using implanted nanocomposite sensor networks and enhanced
           reconstruction algorithm for the probabilistic inspection of damage
           imaging

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      Authors: Jianwei Yang, Yiyin Su, Yaozhong Liao, Pengyu Zhou, Lei Xu, Zhongqing Su
      Abstract: Structural Health Monitoring, Ahead of Print.
      Irrespective of the popularity and demonstrated effectiveness of ultrasound tomography (UT) for damage evaluation, reconstruction of a precise tomographic image can only be guaranteed when a dense transducer network is used. However, a network using transducers such as piezoelectric wafers integrated with the structure under inspection unavoidably lowers local material strength and consequently degrades structural integrity. With this motivation, an implantable, nanocomposite-inspired, piezoresistive sensor network is developed for implementing in situ UT-based structural health monitoring of carbon fibre–reinforced polymer (CFRP) laminates. Individual sensors in the network are formulated with graphene nanosheets and polyvinylpyrrolidone, fabricated using a spray deposition process and circuited via highly conductive carbon nanotube fibres as wires, to form a dense sensor network. Sensors faithfully respond to ultrasound signals of megahertz. With ignorable intrusion to the host composites, the implanted sensor network, in conjunction with a UT approach that is enhanced by a revamped reconstruction algorithm for the probabilistic inspection of damage–based imaging algorithm, has proven capability of accurately imaging anomaly in CFRP laminates and continuously monitoring structural health status, while not at the cost of sacrificing the composites’ original integrity.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-09T04:05:23Z
      DOI: 10.1177/14759217211023930
       
  • Sparse damage detection via the elastic net method using modal data

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      Authors: Rongrong Hou, Xiaoyou Wang, Yong Xia
      Abstract: Structural Health Monitoring, Ahead of Print.
      The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-04T07:19:33Z
      DOI: 10.1177/14759217211021938
       
  • A two-step method for delamination detection in composite laminates using
           experience-based learning algorithm

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      Authors: Tongyi Zheng, Weili Luo, Huawei Tong, Xing Liang
      Abstract: Structural Health Monitoring, Ahead of Print.
      Delamination in composite laminates reduces the structural stiffness and thus causes changes in the vibration responses of the laminates. Therefore, it is feasible to employ dynamic characteristics (such as natural frequencies and mode shapes) for delamination detection by using an optimization method. In the present study, a two-step method is proposed for the delamination detection in composite laminates using an experience-based learning algorithm. In the first step, one-dimensional equivalent through-thickness beam elements are employed to model the composite laminated beam and potential delamination locations are identified. In the second step, a typical three-dimensional finite mesh is utilized for the beam’s modeling and the detailed delamination information (including the delamination location, size, and interface layer) is detected. This two-step method combines the advantages of the two different modeling techniques and is able to significantly reduce the computational cost without reducing detection accuracy. The proposed method is applied for an eight-layer quasi-isotropic symmetric (0/-45/45/90)s composited beam with different delamination situations to verify its effectiveness and robustness. The performance of the two-step method is demonstrated by comparing with the one-step method and other three state-of-the-art algorithms (CMFOA, PSO, and SSA). Moreover, the influence of artificial noise on the accuracy of the detection performance is also investigated. Both numerical and experimental results confirm the superiority of the proposed method for delamination detection in composite laminates especially for the prediction of delamination interface.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-04T06:45:04Z
      DOI: 10.1177/14759217211018114
       
  • A spatial association-coupled double objective support vector machine
           prediction model for diagnosing the deformation behaviour of high arch
           dams

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      Authors: Shaowei Wang, Cong Xu, Yi Liu, Bangbin Wu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Displacement is the most intuitive reflection of the structural behaviour of concrete dams, and it is of great significance to predict future displacement for dam health diagnosis. The mathematical models used to predict displacement are mostly established with the only regression objective of minimizing the mean square error between the measured and fitted displacements, whereas the spatial associations between the displacements of multiple monitoring points of an arch dam are ignored. To increase the prediction accuracy of machine learning technique-based mathematical models, a spatial association-coupled support vector machine model is proposed in this article to predict the displacement of high arch dams. This approach is conducted by performing an incremental distance-based spatial clustering for dam displacement field in the first step. The displacement spatial association is quantified by the integrated shape similarity index between the measured time series of multiple monitoring points and is then coupled with the fitting mean square error to optimize the parameters of the support vector machine model. A case study of an engineering example indicates that the prediction accuracy and generalization ability of the proposed double objective support vector machine model have been greatly improved compared to the traditional single objective support vector machine model. For the total 34 plumb line monitoring points on the dam body of the Jinping-I arch dam, when using the hydraulic, seasonal and time- and hydraulic, hysteretic, seasonal and time-based double objective support vector machine models, the prediction accuracy of 25 and 21 monitoring points increases with an average rate of 50.8% and 47.4%, and the degrees of overfitting are evenly reduced by 44.3% and 70.9%, respectively.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-04T06:42:56Z
      DOI: 10.1177/14759217211017030
       
  • Continuous missing data imputation with incomplete dataset by generative
           adversarial networks–based unsupervised learning for long-term bridge
           health monitoring

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      Authors: Huachen Jiang, Chunfeng Wan, Kang Yang, Youliang Ding, Songtao Xue
      Abstract: Structural Health Monitoring, Ahead of Print.
      Wireless sensors are the key components of structural health monitoring systems. During the signal transmission, sensor failure is inevitable, among which, data loss is the most common type. Missing data problem poses a huge challenge to the consequent damage detection and condition assessment, and therefore, great importance should be attached. Conventional missing data imputation basically adopts the correlation-based method, especially for strain monitoring data. However, such methods often require delicate model selection, and the correlations for vehicle-induced strains are much harder to be captured compared with temperature-induced strains. In this article, a novel data-driven generative adversarial network (GAN) for imputing missing strain response is proposed. As opposed to traditional ways where correlations for inter-strains are explicitly modeled, the proposed method directly imputes the missing data considering the spatial–temporal relationships with other strain sensors based on the remaining observed data. Furthermore, the intact and complete dataset is not even necessary during the training process, which shows another great superiority over the model-based imputation method. The proposed method is implemented and verified on a real concrete bridge. In order to demonstrate the applicability and robustness of the GAN, imputation for single and multiple sensors is studied. Results show the proposed method provides an excellent performance of imputation accuracy and efficiency.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-04T03:27:37Z
      DOI: 10.1177/14759217211021942
       
  • Structural damage detection method based on the complete ensemble
           empirical mode decomposition with adaptive noise: a model steel truss
           bridge case study

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      Authors: Asma Alsadat Mousavi, Chunwei Zhang, Sami F Masri, Gholamreza Gholipour
      Abstract: Structural Health Monitoring, Ahead of Print.
      Signal processing is one of the essential components in vibration-based approaches and damage detection for structural health monitoring. Since signals in the real world are often nonlinear and non-stationary, especially in extended and complex structures, such as bridges, the Hilbert–Huang transform is used for damage assessment. In recent years, the empirical mode decomposition technique has been gradually used in structural health monitoring and damage detection. In this article, the application of complete ensemble empirical mode decomposition with adaptive noise technique is investigated to identify the presence, location, and severity of damage on a steel truss bridge model. The target is built at laboratory conditions and experimentally subjected to white noise excitations. By employing complete ensemble empirical mode decomposition with adaptive noise technique, four key features extracted from the intrinsic mode functions, including energy, instantaneous amplitude, unwrapped phase, and instantaneous frequency, are assessed to localization, quantification, and detection of damage both quantitatively and qualitatively. In addition, to further explore the sensitivity of the damage detection approach based on the complete ensemble empirical mode decomposition with adaptive noise technique method, several improved damage indices are proposed based on the combinations of two statistical time-history features, including kurtosis and entropy features with the energy and instantaneous amplitude features of the analyzed signal. The experimental results from the damage indices based on the extracted features demonstrate the robustness, superiority, and more sensitivity of the complete ensemble empirical mode decomposition with adaptive noise technique method in addressing the damage location, classifying the severity, and detecting the damage compared to empirical mode decomposition and ensemble empirical mode decomposition techniques.
      Citation: Structural Health Monitoring
      PubDate: 2021-06-01T06:25:07Z
      DOI: 10.1177/14759217211013535
       
  • Guided wave–based rail flaw detection technologies: state-of-the-art
           review

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      Authors: Hao Ge, David Chua Kim Huat, Chan Ghee Koh, Gonglian Dai, Yang Yu
      Abstract: Structural Health Monitoring, Ahead of Print.
      The unavoidable increase in train speed and load, as well as the aging of railway facilities, is requiring more and more attention to rail defects detection. As a promising tool for rail, in-service high-speed inspection, guided wave–based detection technologies have been developed in succession by researches in the past two decades. However, there is a lack of a systematic review on the developments and performances of these technologies. This article reviews ultrasonic rail inspection methods comprehensively with the focus on the state-of-the-art technologies based on guided wave. Different excitation options, including train wheel, electromagnetic acoustic transducer, pulsed laser, air-coupled, and contact piezoelectric transducer, are described, respectively, along with their inspection sensitivities, regions, and potential speeds. Finally, future challenges and prospects are discussed to a certain extent to provide references for researchers in this area.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-29T07:39:20Z
      DOI: 10.1177/14759217211013110
       
  • Acoustic emission source characterisation of chloride-induced corrosion
           damage in reinforced concrete

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      Authors: Charlotte Van Steen, Hussein Nasser, Els Verstrynge, Martine Wevers
      Abstract: Structural Health Monitoring, Ahead of Print.
      Worldwide, asset managers are struggling with the management of ageing infrastructure in reinforced concrete. Early detection of reinforcement corrosion, which is generally considered as the major problem, can help to perform dedicated maintenance and repair. The acoustic emission technique is promising to reach this goal. However, research on the characterisation of the different damage sources during corrosion in reinforced concrete remains scarce. In this article, the characterisation of damage processes is investigated on small reinforced concrete prisms and upscaled to reinforced concrete beams under accelerated conditions in a laboratory environment. Damage sources are assigned based on careful validation with crack width measurements and dummy samples. Signals originating from different acoustic emission sources are compared in the time and frequency domain. Moreover, the continuous wavelet transform is applied to provide information on time–frequency characteristics. The results show that the moment of concrete macro-cracking can be derived from a sudden increase of the cumulative acoustic emission events and cumulative acoustic emission energy. However, validation with crack measurements is required. The shift in both peak and centre frequency of the acoustic emission signals is found to be a better indicator. Wavelet transform allows to distinguish acoustic emission sources when frequency ranges are overlapping. Possible acoustic emission sources such as the corrosion process and concrete cover cracking, are successfully assigned. The major contributions of this article are the characterisation of acoustic emission sources from corrosion damage in reinforced concrete, validation with crack measurements and dummy samples, as well as a dedicated wavelet analysis.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-29T07:39:20Z
      DOI: 10.1177/14759217211013324
       
  • Distribution adaptation deep transfer learning method for cross-structure
           health monitoring using guided waves

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      Authors: Bin Zhang, Xiaobin Hong, Yuan Liu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Deep learning algorithm can effectively obtain damage information using labeled samples, and has become a promising feature extraction tool for ultrasonic guided wave detection. But it is difficult to apply the monitoring expertise of structure A to structure B in most cases due to the differences in the dispersion and receiving modes of different waveguides. For multi-structure monitoring at the system level, how to transfer a trained structural health monitoring model to another different structure remains a major challenge. In this article, a cross-structure ultrasonic guided wave structural health monitoring method based on distribution adaptation deep transfer learning is proposed to solve the feature generalization problem in different monitoring structures. First, the joint distribution adaptation method is employed to adapt both the marginal distribution and conditional distribution of the guided wave signals from different structures. Second, convolutional long short-term memory network is constructed to learn the mapping relationship from adapted training samples in source domain. Batch normalization layer is implemented to balance the input tensors of each sample to the same distribution. Finally, the multi-sensor damage indexes are utilized to visually present the damage by probability imaging. The experimental results show that proposed method can utilize the single-sensor monitoring data in one structure to implement the multi-sensor damage monitoring in another structure and achieve the damage imaging visualization. The imaging performance is significantly superior to the existing principal component analysis, transfer component analysis, and other state-of-art comparison methods.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-29T07:39:10Z
      DOI: 10.1177/14759217211010709
       
  • Condition identification of bolted connections using a virtual viscous
           damper

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      Authors: Suryakanta Biswal, Marios K Chryssanthopoulos, Ying Wang
      Abstract: Structural Health Monitoring, Ahead of Print.
      Vibration-based condition identification of bolted connections can benefit the effective maintenance and operation of steel structures. Existing studies show that modal parameters are not sensitive to such damage as loss of preload. In contrast, structural responses in the time domain contain all the information regarding a structural system. Therefore, this study aims to exploit time-domain data directly for condition identification of bolted connection. Finite element model updating is carried out based on the vibration test data of a steel frame, with various combinations of bolts with loss of preload, representing different damage scenarios. It is shown that the match between the numerically simulated and measured acceleration responses of the steel frame cannot be achieved. The reason is that time-dependent nonlinearity is generated in bolted connections during dynamic excitation of the steel frame. To capture the nonlinearity, a virtual viscous damper is proposed. By using the proposed damper alongside the updated system matrices of the finite element model, the time-domain acceleration responses are estimated with great consistency with the measured responses. The results demonstrate that the proposed virtual damper is not only effective in estimating the time-domain acceleration responses in each damage case, but also has the potential for condition identification of bolted connections with such small damage as just one bolt with loss of preload. It can also be applied to other challenging scenarios of condition identification, where modal parameters are not sensitive to the damage.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-29T07:38:43Z
      DOI: 10.1177/14759217211009217
       
  • Fatigue damage detection from imbalanced inspection data of Lamb wave

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      Authors: Jingjing He, Ziwei Fang, Jie Liu, Fei Gao, Jing Lin
      Abstract: Structural Health Monitoring, Ahead of Print.
      The core of structural health monitoring is to provide a real-time monitoring, inspection, and damage detection of structures. As one of the most promising technology to structural health monitoring, the Lamb wave method has attracted interest because it is sensitive to small-scale damage with a long detection range. However, in many real-world structural health monitoring applications, the nature of the problem implies structures work under normal condition in most of its operating phases; therefore, classes of data collected are not equally represented. The predictive capability of damage detection algorithms may significantly be impaired by class imbalance. This article presents a damage detection method using imbalanced inspection data which is collected through Lamb wave detection. Aiming at maximizing detection accuracy, an improved synthetic minority over-sampling technique using three-point triangle (triangle synthetic minority over-sampling technique) is proposed to conduct the over-sampling procedure and increase the number of minority samples. The iterative-partitioning filter is employed to remove the noisy examples which may be introduced by triangle synthetic minority over-sampling technique. Three conventional classification methods, namely, support vector machine, decision tree, and k-nearest neighbor, are used to perform the damage detection. A fatigue crack detection test using Lamb wave is performed to demonstrate the overall procedure of the proposed method. Three damage sensitive features, namely, normalized amplitude, correlation coefficient, and normalized energy, are extracted from signals as datasets. A cross-validation is performed to verify the performance of the proposed method for crack size identification.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-24T02:52:23Z
      DOI: 10.1177/14759217211015243
       
  • The installation of embedded ultrasonic transducers inside a bridge to
           monitor temperature and load influence using coda wave interferometry
           technique

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      Authors: Xin Wang, Ernst Niederleithinger, Iris Hindersmann
      Abstract: Structural Health Monitoring, Ahead of Print.
      This article presents a unique method of installing a special type of embedded ultrasonic transducers inside a 36-m-long section of an old bridge in Germany. A small-scale load test was carried out by a 16 ton truck to study the temperature and load influence on the bridge, as well as the performance of the embedded transducers. Ultrasonic coda wave interferometry technique, which has high sensitivity in detecting subtle changes in a heterogeneous medium, was used for the data evaluation and interpretation. The separation of two main influence factors (load effect and temperature variation) is studied, and future applications of wave velocity variation rate [math] for structural health condition estimation are discussed. As a preliminary research stage, the installation method and the performance of the ultrasonic transducer are recognized. Load- and temperature-induced weak wave velocity variations are successfully detected with a high resolution of 10−4%. The feasibility of the whole system for long-term structural health monitoring is considered, and further research is planned.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-19T07:02:27Z
      DOI: 10.1177/14759217211014430
       
  • A semi-supervised self-training method to develop assistive intelligence
           for segmenting multiclass bridge elements from inspection videos

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      Authors: Muhammad Monjurul Karim, Ruwen Qin, Genda Chen, Zhaozheng Yin
      Abstract: Structural Health Monitoring, Ahead of Print.
      Bridge inspection is an important step in preserving and rehabilitating transportation infrastructure for extending their service lives. The advancement of mobile robotic technology allows the rapid collection of a large amount of inspection video data. However, the data are mainly the images of complex scenes, wherein a bridge of various structural elements mix with a cluttered background. Assisting bridge inspectors in extracting structural elements of bridges from the big complex video data, and sorting them out by classes, will prepare inspectors for the element-wise inspection to determine the condition of bridges. This article is motivated to develop an assistive intelligence model for segmenting multiclass bridge elements from the inspection videos captured by an aerial inspection platform. With a small initial training dataset labeled by inspectors, a Mask Region-based Convolutional Neural Network pre-trained on a large public dataset was transferred to the new task of multiclass bridge element segmentation. Besides, the temporal coherence analysis attempts to recover false negatives and identify the weakness that the neural network can learn to improve. Furthermore, a semi-supervised self-training method was developed to engage experienced inspectors in refining the network iteratively. Quantitative and qualitative results from evaluating the developed deep neural network demonstrate that the proposed method can utilize a small amount of time and guidance from experienced inspectors (3.58 h for labeling 66 images) to build the network of excellent performance (91.8% precision, 93.6% recall, and 92.7% f1-score). Importantly, the article illustrates an approach to leveraging the domain knowledge and experiences of bridge professionals into computational intelligence models to efficiently adapt the models to varied bridges in the National Bridge Inventory.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-19T06:57:57Z
      DOI: 10.1177/14759217211010422
       
  • Gas pipeline event classification based on one-dimensional convolutional
           neural network

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      Authors: Yang An, Xueyan Ma, Xiaocen Wang, Zhigang Qu, Xixin Zhu, Wuliang Yin
      Abstract: Structural Health Monitoring, Ahead of Print.
      Pipeline block and pipeline leak may lead to serious accidents and cause huge economic losses, which have been urgent problems for gas transportation. In this work, active acoustic pulse-compression technology is first introduced to detect and locate these two anomalous events. The matched filtered signals are then normalized and input into one-dimensional convolutional neural network to achieve classification of not only pipeline block and pipeline leak but also normal event such as pipeline elbow which causes acoustic wave reflection as well. Neural network parameter optimization has also been carried out as well as the comparison with long- and short-term memory network. Experimental results demonstrate that compared with long- and short-term memory network, one-dimensional convolutional neural network has an improvement in efficiency due to the great reduction of running time. For non-aliasing pipeline events, both of the models can reach 100% classification accuracy. For aliasing pipeline events, despite the shorter time series and fewer features, the classification accuracy of one-dimensional convolutional neural network still reaches 100.00%, but that of long- and short-term memory network is only 93.89%. Furthermore, the smoothing and slight fluctuation of receiver operating characteristic curve and the high value of area under curve also verify the stability and good classification performance of the proposed trained model. Therefore, the one-dimensional convolutional neural network shows significant performance for pipeline events classification and has considerable potential and application prospect in gas pipeline safety monitoring.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-15T09:05:51Z
      DOI: 10.1177/14759217211010270
       
  • Simulated Lamb wave propagation method for nondestructive monitoring of
           matrix cracking in laminated composites

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      Authors: A Mardanshahi, MM Shokrieh, S Kazemirad
      Abstract: Structural Health Monitoring, Ahead of Print.
      The estimation of the damping coefficient may help to improve the damage detection in composite materials. The purpose of this study was to develop the simulated Lamb wave propagation method for nondestructive monitoring of matrix cracking in laminated composites via the accurate estimation of their damping coefficient. Cross-ply composite specimens with different crack densities were fabricated and tested by the Lamb wave propagation technique. The phase velocity of the Lamb wave and the damping coefficient of the specimens were measured. The finite element models were developed at micro-scale (representative volume elements) and macro-scale (laminated specimens) levels to simulate the Lamb wave propagation in composite specimens. An optimization process was performed through the model updating procedure to achieve finite element models that were in good agreement with experiments. The phase velocity and damping coefficient, obtained from the updated FE models for two crack densities other than those used in the model updating procedure, were successfully examined by experimental results. It was also revealed that the damping coefficient and the rate of increase in the damping coefficient in terms of the crack density were higher for the composite laminates with a higher number of 90° layers. The damping of the fiber–matrix interphase and crack regions were considered in the model and shown as a significant contribution to the overall damping of the composite specimens. The proposed simulated Lamb wave propagation method can be used as a virtual lab for in-situ monitoring of laminated composites with different material properties, stacking sequences, and crack densities.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-13T01:45:06Z
      DOI: 10.1177/14759217211008620
       
  • Optimum wavelet selection for nonparametric analysis toward structural
           health monitoring for processing big data from sensor network: A
           comparative study

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      Authors: Ahmed Silik, Mohammad Noori, Wael A Altabey, Ji Dang, Ramin Ghiasi, Zhishen Wu
      Abstract: Structural Health Monitoring, Ahead of Print.
      A critical problem encountered in structural health monitoring of civil engineering structures, and other structures such as mechanical or aircraft structures, is how to convincingly analyze the nonstationary data that is coming online, how to reduce the high-dimensional features, and how to extract informative features associated with damage to infer structural conditions. Wavelet transform among other techniques has proven to be an effective technique for processing and analyzing nonstationary data due to its unique characteristics. However, the biggest challenge frequently encountered in assuring the effectiveness of wavelet transform in analyzing massive nonstationary data from civil engineering structures, and in structural health diagnosis, is how to select the right wavelet. The question of which wavelet function is appropriate for processing and analyzing the nonstationary data in civil engineering structures has not been clearly addressed, and no clear guidelines or rules have been reported in the literature to show how the right wavelet is chosen. Therefore, this study aims to address an important question in this regard by proposing a new framework for choosing a proper wavelet that can be customized for massive nonstationary data analysis, disturbances separation, and extraction of informative features associated with damage. The proposed method takes into account data type, data and wavelet characteristics, similarity, sharing information, and data recovery accuracy. The novelty of this study lies in integrating multi-criteria which are associated directly with features that correlated well with change in structures due to damage, including common criteria such as energy, entropy, linear correlation index, and variance. Also, it introduces and considers new proposed measures, such as wavelet-based nonlinear correlation such as cosh spectral distance and mutual information, wavelet-based energy fluctuation, measures-based recovery accuracy, such as sensitive feature extraction, noise reduction, and others to evaluate various base wavelets’ function capabilities for appropriate decomposition and reconstruction of structural dynamic responses. The proposed method is verified by experimental and simulated data. The results revealed that the proposed method has a satisfactory performance for base wavelet selection and the small order of Daubechies and Symlet provide the best results, especially order 3. The idea behind our proposed framework can be applied to other structural applications.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-10T06:12:05Z
      DOI: 10.1177/14759217211010261
       
  • A portable bolt-loosening detection system with piezoelectric-based
           nondestructive method and artificial neural networks

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      Authors: Wongi S Na
      Abstract: Structural Health Monitoring, Ahead of Print.
      In general, the bolted joints that connect and secure components together can be easily spotted in our surroundings. This joining method has been commonly used in various areas of engineering (e.g. aerospace, civil, and mechanical engineering) as it has been proven one of the most effective means to join parts together. Although it has its advantages, the vibration that bolted structures endure during service ultimately causes the bolts to loosen. This can, in turn, have a negative effect on the structure’s safety and may, at worst, cause it to fail. Routine inspections of structures are conducted on a regular basis, with some inspection categories requiring heavy equipment in order to acquire certain data. In addition, monitoring systems can be expensive to install and maintain, especially in large infrastructures. In an effort to rectify this, in this study, a piezoelectric transducer–based nondestructive technique is used in conjunction with the application of a reattachable device to investigate the possibility of creating a low-cost inspection system. The acquired data were processed by means of an artificial neural network technique that showed promising results in terms of mitigating bolt loosening.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-08T07:04:08Z
      DOI: 10.1177/14759217211008619
       
  • Understanding the interaction of the fundamental Lamb-wave modes with
           material discontinuity: finite element analysis and experimental
           validation

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      Authors: Mohammad Ali Fakih, Samir Mustapha, Mohammad Harb, Ching-Tai Ng
      Abstract: Structural Health Monitoring, Ahead of Print.
      The interaction of guided waves with a material discontinuity is not well understood. This study investigates the propagation behavior of the fundamental Lamb-wave modes, the symmetric mode (S0) and the anti-symmetric mode (A0), upon interaction with welded joints of dissimilar materials. A plate with an intact AA6061-T6/AZ31B dissimilar joint was employed, and the interaction of the propagating wave with the material interface was scrutinized numerically and validated experimentally. Plane-wave approximation was also adopted to investigate the behavior of the symmetric modes, and its performance was compared to the numerical and experimental results. The effect of the angle of incidence on the reflection, transmission, and mode conversion of the incident modes was analyzed. The study was conducted as the excited Lamb wave propagated from AA6061-T6 to AZ31B (forward), and when the propagation direction was reversed (backward). Different techniques were developed to identify the in-plane and out-of-plane modes from the three-dimensional measurements and to separate wave reflections and transmissions of the joint. The fundamental shear-horizontal guided-wave mode (SH0 mode) has evolved upon the interaction of the obliquely-incident Lamb-wave S0 mode with the interface. While the reflection of the SH0 mode from the joint was found to be well-pronounced, its transmission to the other material is extremely weak. The analytical solution, using plane-wave approximation, was accurate for predicting the behavior of the in-plane modes (S0 and S0–SH0 modes). Despite the peaks appearing at the critical angle, the absolute values of the reflection coefficients of the studied modes have shown similar trends between the forward and the backward propagation directions. The total reflection of the excited wave, from the material interface, was not observed in any condition. The transmission coefficients of the S0 and A0 modes are almost constant until reaching very steep incidence angles [math]. The results were experimentally validated on an intact AA6061-T6/AZ31B friction-stir-welded joint using an excitation frequency of 200 kHz. Measurements along the transmission and reflection directions were conducted using a three-dimensional scanning laser vibrometer. Experimental results showed very good agreement with both the analytical and the numerical ones.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-07T11:55:19Z
      DOI: 10.1177/14759217211007118
       
  • Research on dynamic behavior and traffic management decision-making of
           suspension bridge after vortex-induced vibration event

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      Authors: Danhui Dan, Xuewen Yu, Fei Han, Bin Xu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Long-span suspension bridges are susceptible to wind loads due to their lightweight, low stiffness, and small structural damping. Recently, two large-span suspension bridges in China that closed for several months due to COVID-2019 experienced large-scale and continuous vortex-induced vibration shortly after reopening to traffic, and the traffic was closed again for safety consideration, which has aroused widespread concerns in society. To provide a reference for owners and related decision-making departments whether to restore the traffic, this article intends to explore the impact mechanism of traffic loads on the dynamic behavior of suspension bridges. First, two mechanical models for suspension bridges considering traffic loads and structural damping are proposed in this article. Then, based on the extended dynamic stiffness method, the explicit expressions of modal damping ratio in the two models are derived for the first time. Subsequently, Wittrick–Williams algorithm is employed to solve the frequency equation to obtain the modal frequency of the structure that considers the effect of traffic loads. A numerical case is studied to inspect the influence of traffic loads on the structural dynamic characteristics. Moreover, field monitoring data of accelerations of a suspension bridge are utilized to demonstrate the reasonability and accuracy of the approach proposed. Analysis shows that the theoretical results are consistent well with the measured ones, which indicates the traffic loads will affect the dynamic characteristics of the suspension bridge, thus reducing the modal frequency and increasing the modal damping ratio. Besides, the measured results further explain that the contribution of traffic loads to the structural damping is significant, which has a positive effect on preventing and eliminating vortex-induced vibration response. Some interesting and enlightening conclusions are also obtained in this article.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-05T07:09:30Z
      DOI: 10.1177/14759217211011582
       
  • Polynomial Chaos-Kriging metamodel for quantification of the debonding
           area in large wind turbine blades

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      Authors: Bruna Pavlack, Jessé Paixão, Samuel da Silva, Americo Cunha, David García Cava
      Abstract: Structural Health Monitoring, Ahead of Print.
      This study aims to investigate the performance of a data-driven methodology for quantifying damage based on the use of a metamodel obtained from the Polynomial Chaos-Kriging method. The investigation seeks to quantify the severity of the damage, described by a specific type of debonding in a wind turbine blade as a function of a damage index. The damage indexes used are computed using a data-driven vibration-based structural health monitoring methodology. The blade’s debonding damage is introduced artificially, and the blade is excited with an electromechanical actuator that introduces a mechanical impulse causing the impact on the blade. The acceleration responses’ vibrations are measured by accelerometers distributed along the trailing and the wind turbine blade. A metamodel is formerly obtained through the Polynomial Chaos-Kriging method based on the damage indexes, trained with the blade’s healthy condition and four damage conditions, and validated with the other two damage conditions. The Polynomial Chaos-Kriging manifests promising results for capturing the proper trend for the severity of the damage as a function of the damage index. This research complements the damage detection analyses previously performed on the same blade.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-04T06:26:01Z
      DOI: 10.1177/14759217211007956
       
  • A novel intelligent inspection robot with deep stereo vision for
           three-dimensional concrete damage detection and quantification

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      Authors: Cheng Yuan, Bing Xiong, Xiuquan Li, Xiaohan Sang, Qingzhao Kong
      Abstract: Structural Health Monitoring, Ahead of Print.
      Crack assessment of reinforced concrete structures using stereo cameras is a potential way for increasing the efficiency and safety of infrastructure maintenance routines. However, existing damage methods for reinforced concrete structures are based on the segmentation of two-dimensional planes without consideration to the actual size of concrete damage. Furthermore, on-site structural monitoring requires the installation of a large number of contact-based sensing devices, resulting in the potentially excessive consumption of time and financial resources. Therefore, a new vision-based damage assessment method for reinforced concrete structures using a novel intelligent inspection robot with Internet of things–enabled data communication system is proposed in this article. In the first part of this article, the data acquisition system of the inspection robot and the algorithm for three-dimensional structural reconstruction using a stereo camera is discussed. The discussion is followed by a description of the method for crack quantification based on a new proposed deep-learning technique. Finally, to accomplish damage localization, the quantified concrete damage with actual size information is projected onto a three-dimensional surface point cloud reconstruction of the inspected structure. To verify the proposed method, a reinforced concrete column that has undergone cyclic loading failure is used as an inspection subject. The validation experiment demonstrated the ability of the proposed system to segment, localize, and quantify the damage in three-dimensional space with high accuracy.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-04T06:23:23Z
      DOI: 10.1177/14759217211010238
       
  • A new dam structural response estimation paradigm powered by deep learning
           and transfer learning techniques

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      Authors: Yangtao Li, Tengfei Bao, Zhixin Gao, Xiaosong Shu, Kang Zhang, Lunchen Xie, Zhentao Zhang
      Abstract: Structural Health Monitoring, Ahead of Print.
      With the rapid development of information and communication techniques, dam structural health assessment based on data collected from structural health monitoring systems has become a trend. This allows for applying data-driven methods for dam safety analysis. However, data-driven models in most related literature are statistical and shallow machine learning models, which cannot capture the time series patterns or learn from long-term dependencies of dam structural response time series. Furthermore, the effectiveness and applicability of these models are only validated in a small data set and part of monitoring points in a dam structural health monitoring system. To address the problems, this article proposes a new modeling paradigm based on various deep learning and transfer learning techniques. The paradigm utilizes one-dimensional convolutional neural networks to extract the inherent features from dam structural response–related environmental quantity monitoring data. Then bidirectional gated recurrent unit with a self-attention mechanism is used to learn from long-term dependencies, and transfer learning is utilized to transfer knowledge learned from the typical monitoring point to the others. The proposed paradigm integrates the powerful modeling capability of deep learning networks and the flexible transferability of transfer learning. Rather than traditional models that rely on experience for feature selection, the proposed deep learning–based paradigm directly utilizes environmental monitoring time series as inputs to accurately estimate dam structural response changes. A high arch dam in long-term service is selected as the case study, and three monitoring items, including dam displacement, crack opening displacement, and seepage are used as the research objects. The experimental results show that the proposed paradigm outperforms conventional and shallow machine learning–based methods in all 41 tested monitoring points, which indicates that the proposed paradigm is capable of dealing with dam structural response estimation with high accuracy and robustness.
      Citation: Structural Health Monitoring
      PubDate: 2021-05-03T09:10:14Z
      DOI: 10.1177/14759217211009780
       
  • Fusion-based damage diagnostics for stiffened composite panels

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      Authors: Agnes Broer, Georgios Galanopoulos, Rinze Benedictus, Theodoros Loutas, Dimitrios Zarouchas
      Abstract: Structural Health Monitoring, Ahead of Print.
      Conducting damage diagnostics on stiffened panels is commonly performed using a single SHM technique. However, each SHM technique has both its strengths and limitations. Rather than straining the expansion of single SHM techniques going beyond their intrinsic capacities, these strengths and limitations should instead be considered in their application. In this work, we propose a novel fusion-based methodology between data from two SHM techniques in order to surpass the capabilities of a single SHM technique. The aim is to show that by considering data fusion, a synergy can be obtained, resulting in a comprehensive damage assessment, not possible using a single SHM technique. For this purpose, three single-stiffener carbon–epoxy panels were subjected to fatigue compression after impact tests. Two SHM techniques monitored damage growth under the applied fatigue loads: acoustic emission and distributed fiber optic strain sensing. Four acoustic emission sensors were placed on each panel, thereby allowing for damage detection, localization, type identification (delamination), and severity assessment. The optical fibers were adhered to the stiffener feet’ surface, and its strain measurements were used for damage detection, disbond localization, damage type identification (stiffness degradation and disbond growth), and severity assessment. Different fusion techniques are presented in order to integrate the acoustic emission and strain data. For damage detection and severity assessment, a hybrid health indicator is obtained by feature-level fusion while a complementary and cooperative fusion of the diagnostic results is developed for damage localization and type identification. We show that damage growth can be monitored up until final failure, thereby performing a simultaneous damage assessment on all four SHM levels. In this manner, we demonstrate that by proposing a fusion-based approach toward SHM of composite structures, the intrinsic capacity of each SHM technique can be utilized, leading to synergistic effects for damage diagnostics.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-24T08:55:08Z
      DOI: 10.1177/14759217211007127
       
  • Sub-surface metal loss defect detection using cold thermography and
           dynamic reference reconstruction (DRR)

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      Authors: Siavash Doshvarpassand, Xiangyu Wang, Xianzhong Zhao
      Abstract: Structural Health Monitoring, Ahead of Print.
      Corrosion is considered a destructive phenomenon that affects almost all metals. Active infrared thermography is an online (no result delay) and non-intrusive (no process disruption) method of non-destructive testing (NDT), which has shown profound capabilities of detecting sub-surface metal loss. However, thermal reflections, non-uniform stimulation and lateral heat diffusion will remain as the most undesirable phenomena preventing the effective observation of sub-surface defects. This becomes more challenging when there is no a priori knowledge of the anomalies to effectively distinguish between defective and non-defective areas. In this work, cooling stimulation is considered as the thermal excitation mean as 1- a very few reports in this regard have been mentioned in the body of literature and 2- a dynamic setup was achieved that is found to be effective to minimise the possibility of disrupting reflections or artefacts registered by thermal camera similar to the case of using heating stimulation. A state-of-the-art prototype mechanism was manufactured. This equipment includes a carrier carrying a thermal camera and a cooling medium reservoir operating in reciprocating motion setup. This equipment is able to scan the test piece while cold stimulation is in operation, and immediately after that the camera registers the thermal evolution. An automated contrast enhancement pipeline using a variation of adaptive histogram equalisation (AHE) combined with principal component analysis (PCA) method was developed. The enhanced image results demonstrated the capability of accurately detecting sub-surface metal loss as low as 37.5% as well as an efficiently reconstructed reference (non-defective) area.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-24T07:00:32Z
      DOI: 10.1177/1475921721999599
       
  • Structural damage monitoring for metallic panels based on acoustic
           emission and adaptive improvement variational mode decomposition–wavelet
           packet transform

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      Authors: Yang Li, Feiyun Xu
      Abstract: Structural Health Monitoring, Ahead of Print.
      The metallic panel acoustic emission signal with strong non-stationary properties is normally composed of multiple components (e.g. impulses, background noise, and other external signal), where impulses relevant to metallic panel are easily contaminated by background noise and other external signal, making it difficult to excavate the inherent acoustic emission signal features. To address this issue and achieve the damage monitoring of metallic panels based on acoustic emission technology, a new scheme based on adaptive improvement variational mode decomposition–wavelet packet transform is developed for extracting acoustic emission signal features of metallic panels. Specifically, three different dimensions of Q235B steel plates are utilized to collect acoustic emission signal during three-point bending experiments, to evaluate the effectiveness of the proposed approach and to investigate the influence of size effect on the acoustic emission signal characteristics. In addition, the transient process and centroid frequency distribution of each damage stage are investigated, and the internal structure variations in the bending damage process are detected by scanning electron microscopy inspection. Moreover, the generalization of the proposed damage monitoring method is evaluated for plate-like structures that have complex geometric features, such as welds. The results demonstrate the effectiveness of the proposed method for acoustic emission–based structural health monitoring of metallic plate-like structures.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-23T06:13:50Z
      DOI: 10.1177/14759217211008969
       
  • A novel data-driven method for structural health monitoring under ambient
           vibration and high-dimensional features by robust multidimensional scaling
           

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      Authors: Alireza Entezami, Hassan Sarmadi, Masoud Salar, Carlo De Michele, Ali Nadir Arslan
      Abstract: Structural Health Monitoring, Ahead of Print.
      Dealing with the problem of large volumes of high-dimensional features and detecting damage under ambient vibration are critical to structural health monitoring. To address these challenges, this article proposes a novel data-driven method for early damage detection of civil engineering structures by robust multidimensional scaling. The proposed method consists of some simple but effective computational parts including a segmentation process, a pairwise distance calculation, an iterative algorithm regarding robust multidimensional scaling, a matrix vectorization procedure, and a Euclidean norm computation. AutoRegressive Moving Average models are fitted to vibration time-domain responses caused by ambient excitations to extract the model residuals as high-dimensional features. In order to increase the reliability of damage detection and avoid any false alarm, the extreme value theory is considered to determine a reliable threshold limit. However, the selection of an appropriate extreme value distribution is crucial and troublesome. To cope with this limitation, this article introduces the generalized extreme value distribution and its shape parameter for choosing the best extreme value model among Gumbel, Fréchet, and Weibull distributions. The main contributions of this article include developing a novel data-driven strategy for early damage detection and addressing the limitation of using high-dimensional features. Experimental data sets of two well-known civil structures are utilized to validate the proposed method along with some comparative studies. Results demonstrate that the proposed data-driven method in conjunction with the extreme value theory is highly able to detect damage under ambient vibration and high-dimensional features.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-23T06:09:51Z
      DOI: 10.1177/1475921720973953
       
  • Damage identification of bolt connection in steel truss structures by
           using sound signals

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      Authors: Debing Zhuo, Hui Cao
      Abstract: Structural Health Monitoring, Ahead of Print.
      Different from traditional health-monitoring methods based on vibrational signals recorded by contact sensors, an online diagnosis procedure for steel truss structures using sound signals was proposed. The basic idea of the procedure was to identify the features related to bolt connection damage extracted from sound signals and locate the damaged position. Before the online diagnosis was carried out, sound signals were specifically collected by a microphone array involving environmental noise and sound discharged by artificial damaged bolt connections. Then the signals were preprocessed and their time and frequency domain features were extracted, from which sensitive features were selected by support vector machine recursive feature elimination. A support vector machine classifier aiming to identify signals related to damage was trained with the selected sensitive features, and a genetic algorithm was used to optimize its parameters. An improved method called steered response power and phase transformation with offline database was put forward to compute the steered response power values of coordinates in the offline database to localize the source of identified damage signals. The pre-built database consisted of a series of coordinates indicating the positions of bolts. When the online diagnosis was implemented for a steel truss structure, sound signals were picked up by the microphone array at the same location as that used for the database construction. The signals were preprocessed and their sensitive features were extracted for damage identification by the trained support vector machine classifier. If some signals were judged to be related to bolt connection damage, steered response power and phase transformation with offline database was used to compute steered response power values, with which a fusion decision was made based on evidence theory to locate the damaged bolt connection. The experiment of a steel truss model with 24 bolt connections showed that the proposed procedure could locate the loose bolts precisely even under heavy noise effect, and had a smaller computational load compared with traditional steered response power and phase transformation.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-22T01:03:42Z
      DOI: 10.1177/14759217211004823
       
  • A novel multi-classifier information fusion based on Dempster–Shafer
           theory: application to vibration-based fault detection

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      Authors: Vahid Yaghoubi, Liangliang Cheng, Wim Van Paepegem, Mathias Kersemans
      Abstract: Structural Health Monitoring, Ahead of Print.
      Achieving a high prediction rate is a crucial task in fault detection. Although various classification procedures are available, none of them can give high accuracy in all applications. Therefore, in this article, a novel multi-classifier fusion approach is developed to boost the performance of the individual classifiers. This is acquired by using Dempster–Shafer theory. However, in cases with conflicting evidences, the Dempster–Shafer theory may give counterintuitive results. In this regard, a preprocessing technique based on a new metric is devised in order to measure and mitigate the conflict between the evidences. To evaluate and validate the effectiveness of the proposed approach, the method is applied to 15 benchmarks datasets from UCI and KEEL. Furthermore, it is applied for classifying polycrystalline nickel alloy first-stage turbine blades based on their broadband vibrational response. Through statistical analysis with different noise levels, and by comparing with four state-of-the-art fusion techniques, it is shown that the proposed method improves the classification accuracy and outperforms the individual classifiers.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-21T09:02:42Z
      DOI: 10.1177/14759217211007130
       
  • An automated hypersphere-based healthy subspace method for robust and
           unsupervised damage detection via random vibration response signals

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      Authors: Vamvoudakis-Stefanou Kyriakos, Fassois Spilios, Sakellariou John
      Abstract: Structural Health Monitoring, Ahead of Print.
      A novel, unsupervised, hypersphere-based healthy subspace method for robust damage detection under non-quantifiable uncertainty via a limited number of random vibration response sensors is postulated. The method is based on the approximate construction, within a proper feature space, of a healthy subspace representing the healthy structural dynamics under uncertainty as the union of properly selected hyperspheres. This is achieved via a fully automated algorithm eliminating user intervention, and thus subjective selections, or complex optimization procedures. The main asset of the proposed method lies in combining simplicity and full automation with high performance. Its performance is systematically assessed via two experimental case studies featuring various uncertainty sources and distinct healthy subspace geometries, while interesting comparisons with three well-known robust damage detection methods are also performed. The results indicate excellent detection performance, which also compares favorably to that of alternative methods.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-20T10:24:27Z
      DOI: 10.1177/14759217211004429
       
  • Assessment of ice impact load threshold exceedance in the propulsion shaft
           of an ice-faring vessel via Bayesian inversion

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      Authors: Nico De Koker, Anriëtte Bekker
      Abstract: Structural Health Monitoring, Ahead of Print.
      Ice-induced impact loads on the propeller blades of vessels operating in polar waters represent a notable hazard to ship propulsion systems. Industry guidelines determine the maximum allowable ice-induced propeller moments, but these loads are not practical to measure directly. Recent studies have implemented deterministic inversion methods to determine these impact-induced moments from torque measurements on the propulsion shaft. This study considers this inversion problem from a stochastic perspective. Bayesian inversion is used, first, as a means of determining the optimal regularisation parameters, and second, as an avenue to explore the contributions to uncertainty in the inverted ice loading values due to the linear inversion model. The method is implemented for two sets of shaft-line measurements recorded on the SA Agulhas II, a polar class PC-5 research and supply vessel, via a lumped-mass model for the forward response signal. Using more sophisticated simulation results as prior information for the model uncertainty, it is shown that inverted ice-induced propeller moments around 20% below the industry guideline maximum loading of 1009.9 kN m correspond to a 1% probability of ice-induced moments exceeding this limit. This finding is significant, given the number of propeller impact events for the SA Agulhas II that approached this limit during recent voyages, and highlights the need for considering design specifications for propulsion systems in ice-faring vessels from a risk perspective.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-20T10:14:57Z
      DOI: 10.1177/14759217211004232
       
  • An optimized variational mode extraction method for rolling bearing fault
           diagnosis

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      Authors: Bin Pang, Mojtaba Nazari, Zhenduo Sun, Jiaying Li, Guiji Tang
      Abstract: Structural Health Monitoring, Ahead of Print.
      The fault feature signal of rolling bearing can be characterized as the narrow-band signal with a specific resonance frequency. Therefore, resonance demodulation analysis is a powerful damage detection technique of bearings. In addition to the fault feature signal, the measured vibration signals carry various interference components, and these interference components become a serious obstacle of fault feature extraction. Variational mode extraction is a novel signal analysis method designed to retrieve a specific signal component from the composite signal. Variational mode extraction is founded on a similar basis as variational mode decomposition, while it shows better accuracy and higher efficiency compared with variational mode decomposition. In this study, variational mode extraction is introduced to the resonance demodulation analysis of bearing fault. As the results of variational mode extraction analysis are greatly influenced by the choice of two parameters, that is, the balancing factor α and the initial guess of center frequency ωd, an optimized variational mode extraction method is further developed. First, a new fault information evaluation index for measuring the richness of fault characteristics of the signal, termed ensemble impulsiveness and cyclostationarity, is formulated. Second, the ensemble impulsiveness and cyclostationarity is used as the fitness function of particle swarm optimization to automatically determine the optimal values of α and ωd. Finally, the validity of optimized variational mode extraction method is verified by simulated and experimental analysis, and the superiority of optimized variational mode extraction method is highlighted through comparison with two other advanced resonance demodulation analysis approaches, that is, the improved kurtogram and infogram. The analysis results indicate that optimized variational mode extraction method has a powerful capability of resonance demodulation analysis.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-17T06:46:51Z
      DOI: 10.1177/14759217211006637
       
  • Damage cross detection between bridges monitored within one cluster using
           the difference ratio of projected strain monitoring data

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      Authors: Jianxin Cao, Yang Liu, Changping Li
      Abstract: Structural Health Monitoring, Ahead of Print.
      The bridges monitored within one cluster refer to several medium- and small-span beam bridges with the same or similar structural characteristics located in a local road network, and these bridges suffer the similar traffic and environmental loads. It is still little research on how to detect the damage of all bridges monitored within one cluster utilizing the above-mentioned characteristics of these bridges. To address this issue, a method is proposed for the damage cross-detection between bridges monitored within one cluster by using the difference ratio of projected strain monitoring data under time-varying environmental temperatures. First, a damage feature is established by using the difference ratio of projected strain monitoring data obtained from the same cross-section position of any two bridges monitored within one cluster that have similar or identical structural characteristics. On this basis, the relationship between the statistical characteristics of the proposed damage feature and the degree of structural similarity between two bridges are discussed in detail. Second, a damage detection index is presented by calculating the subspace angle between two damage features. Then, combined with kernel density estimation and a cross-validation strategy, the proposed index is implemented to detect the damage of all bridges monitored within one cluster. Finally, numerical simulation examples are utilized to analyse and discuss the application limitations, noise resistance performance and structural damage sensitivity of the proposed method. Moreover, the effectiveness of the proposed method is also demonstrated by using the strain monitoring data obtained from actual bridges monitored within one cluster.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-17T06:11:00Z
      DOI: 10.1177/14759217211006792
       
  • Stress evaluation in seven-wire strands based on singular value feature of
           ultrasonic guided waves

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      Authors: Qian Ji, Li Jian-Bin, Liu Fan-Rui, Zhou Jian-Ting, Wang Xu
      Abstract: Structural Health Monitoring, Ahead of Print.
      The seven-wire strands are the crucial components of prestressed structures, though their performance inevitably degrades with the passage of time. The ultrasonic guided wave methods have been intensely studied, owing to its tremendous potential for full-scale applications, among the existing nondestructive testing methods, for evaluating the stress status of strands. We have employed the theoretical and finite element methods to solve the dispersion curve of single wire and steel strands under various boundary conditions. Thereafter, the singular value decomposition was adopted to work with the simulated and experimental signals for extracting a feature vector that carries valuable stress status information. The effectiveness of the vector was verified by analyzing the relationship between the vector and the stress level. The vector was also used as an input to establish a support vector regression model. The accuracy of the model has been discussed for different sample sizes. The results show that the fundamental mode dispersion curve offset on the high-frequency part and cut-off frequency increases as the boundary constraints enhance. Simulated and experimental results have demonstrated the effectiveness and potential of the proposed support vector regression method for evaluating the stress level in the strands. This method performs well even at low stress levels and the reliability can be enhanced by adding more samples.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-13T06:31:40Z
      DOI: 10.1177/14759217211005399
       
  • Acoustic inspection system with unmanned aerial vehicles for wind turbines
           structure health monitoring

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      Authors: Fausto Pedro García Márquez, Pedro José Bernalte Sánchez, Isaac Segovia Ramírez
      Abstract: Structural Health Monitoring, Ahead of Print.
      Wind energy is considered as one of the most important renewable energies in the world, employing larger and more complex wind turbines. They need novel condition monitoring systems to ensure the reliability, availability, safety and maintainability of the main components of the wind turbines. It leads to early fault detection, increasing the productivity and minimizing the maintenance costs and downtimes. This article proposes a novel non-destructive testing system to analyse acoustically rotatory devices of wind turbines. It captures the noise emitted by the devices using an acoustic condition monitoring system embedded in an unmanned aerial vehicle. The signal acquired is sent to ground computer station for recording and analysing the data. It uses a test rig, previously validated, to carry out a set of experiments to simulate the main faults. A signal processing method is done by wavelet transforms that filters and analyses the energy patterns of the signals. The results are analysed qualitatively and quantitatively considering different scenarios. A statistical analysis is developed to compare the numerical results provided by different wavelet transform families and convolutional neural network. It is concluded that Symlets and Daubechies families report equivalent results for this case study. The accuracies of the results are more than 75%, reaching up to 100%. The approach is validated employing Friedman test.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-10T10:43:19Z
      DOI: 10.1177/14759217211004822
       
  • Using digital image correlation to evaluate the bond between carbon
           fibre-reinforced polymers and timber

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      Authors: Hugo C Biscaia, João Canejo, Shishun Zhang, Raquel Almeida
      Abstract: Structural Health Monitoring, Ahead of Print.
      The use of optic measurements such as digital image correlation to take strain measurements of fibre-reinforced polymers bonded to a substrate has been on the increase recently. This technique has proven to be useful to fully characterize the bond behaviour between two materials. Although modern digital cameras can take high-definition photos, this task is far from simple due to the tiny displacements that need to be measured. Consequently, digital image correlation measurements lead to relative errors that, at an initial stage of the debonding process, are higher than those calculated close to the debonding of the fibre-reinforced polymer from the substrate. This study aims to evaluate and analyse the use of the digital image correlation technique on the bond between carbon fibre-reinforced polymer laminates and timber when subjected to a pull-out load consistent with fracture Mode II. To allow the quantification of the relative errors obtained from the digital image correlation measurements during the full debonding process, several strain gauges were used to measure the strains in the carbon fibre-reinforced polymer composite. The accuracy of the digital image correlation measurements is analysed by comparing it with those obtained from the strain gauges, which is a very well-established measuring technique. Another contribution of this study is to check the versatility of the digital image correlation measurements on a broader range of situations. To that end, several timber prisms were bonded with seven different bonding techniques with and without the installation of a mechanical anchorage at the carbon fibre-reinforced polymer unpulled end. The results showed that the digital image correlation technique was able to track the slips calculated from the strain gauge measurements until the debonding load, but after that, some difficulties to measure the displacements of the anchored carbon fibre-reinforced polymer-to-timber joints were detected. The digital image correlation technique also over predicted bond stresses when compared with those taken from the strain gauges, which led to bond–slip relationships with higher bond stresses.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-09T06:23:06Z
      DOI: 10.1177/14759217211006021
       
  • Examining the contribution of near real-time data for rapid seismic loss
           assessment of structures

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      Authors: Enrico Tubaldi, Ekin Ozer, John Douglas, Pierre Gehl
      Abstract: Structural Health Monitoring, Ahead of Print.
      This study proposes a probabilistic framework for near real-time seismic damage assessment that exploits heterogeneous sources of information about the seismic input and the structural response to the earthquake. A Bayesian network is built to describe the relationship between the various random variables that play a role in the seismic damage assessment, ranging from those describing the seismic source (magnitude and location) to those describing the structural performance (drifts and accelerations) as well as relevant damage and loss measures. The a priori estimate of the damage, based on information about the seismic source, is updated by performing Bayesian inference using the information from multiple data sources such as free-field seismic stations, global positioning system receivers and structure-mounted accelerometers. A bridge model is considered to illustrate the application of the framework, and the uncertainty reduction stemming from sensor data is demonstrated by comparing prior and posterior statistical distributions. Two measures are used to quantify the added value of information from the observations, based on the concepts of pre-posterior variance and relative entropy reduction. The results shed light on the effectiveness of the various sources of information for the evaluation of the response, damage and losses of the considered bridge and on the benefit of data fusion from all considered sources.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-09T06:15:47Z
      DOI: 10.1177/1475921721996218
       
  • A novel T-shaped sensor cluster for acoustic source localization

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      Authors: Qiang Gao, Jun Young Jeon, Gyuhae Park, Yunde Shen, Jiawei Xiang
      Abstract: Structural Health Monitoring, Ahead of Print.
      This study proposes a new sensor cluster configuration for localizing an acoustic source in a plate using uniform linear array beamforming and T-shaped sensor clusters. This technique requires neither the properties of the plate material nor a dense array of sensors to find the direction of arrival of the acoustic source. It functions by placing four sensors in a cluster in the shape of the letter “‘T” over a small region of the plate. Uniform linear array beamforming-based source localization is carried out by the constructive interference of different sensor signals. However, this approach has the disadvantage of a very low resolution when the direction of arrival approaches certain values. The L-shaped sensor clusters use the information from the time difference of arrival between the sensors to estimate the direction of arrival, which has a high resolution in all directions except for the direction that is very close to vertical to the cluster. In this study, we numerically and experimentally demonstrate that the proposed T-shaped sensor cluster can accurately localize the acoustic source with no blind area. We also detail its superior performance compared to both uniform linear array beamforming and L-shaped clusters. In the experimental investigation, the maximum deviation of impact source localization was reduced significantly, from 54° to 4° for an aluminum plate, and from 42° to 3° for a composite plate. Furthermore, this novel combined sensor array layout requires only a few sensors, which can significantly reduce the cost of structural health monitoring practice.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-05T09:53:50Z
      DOI: 10.1177/14759217211004236
       
  • Broadband nonlinear elastic wave modulation spectroscopy for damage
           detection in composites

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      Authors: Joost Segers, Saeid Hedayatrasa, Gaétan Poelman, Wim Van Paepegem, Mathias Kersemans
      Abstract: Structural Health Monitoring, Ahead of Print.
      A non-destructive testing procedure is proposed for damage detection in composites using full wavefield measurement obtained using a scanning laser Doppler vibrometer. Vibrations are excited using two low-power piezoelectric actuators leading to nonlinear elastic wave modulation at the defect. One actuator is supplied with a broadband chirp signal and the other actuator is supplied with a single-frequency sine signal. First, a time–frequency filtering method is proposed to extract specific nonlinear components of interest (e.g. second higher harmonic and first modulation sideband) without the need for multiple excitation sequences. Next, damage maps are constructed using broadband bandpower calculation of the filtered nonlinear components. It is demonstrated that the modulation sidebands provide an exclusive imaging of defect nonlinearity, and are not affected by potential source nonlinearity. The proposed damage map construction procedure is applied for various carbon fiber reinforced polymer test specimens with different damage features: (1) coupon with quasi-static indentation damage, (2) coupon with artificial delaminations, (3) bicycle frame with impact damage, and (4) stiffened aircraft panel with partially debonded stiffener. The obtained results indicate the high performance of the developed procedure for detection of various defect types in curved and/or stiffened carbon fiber reinforced polymer components.
      Citation: Structural Health Monitoring
      PubDate: 2021-04-05T09:53:17Z
      DOI: 10.1177/14759217211002562
       
  • Bolt damage identification based on orientation-aware center point
           estimation network

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      Authors: Yang Zhang, Ka-Veng Yuen
      Abstract: Structural Health Monitoring, Ahead of Print.
      With the development of deep learning, object detection algorithms based on horizontal box are widely used in the field of damage identification. However, damages can be in any direction and position, and they are not necessarily horizontal or vertical. This article proposes a bolt damage identification network, namely, orientation-aware center point estimation network, which models a damage as a center point of its rotated bounding box. The proposed orientation-aware center point estimation network uses deep layer aggregation network to search center points and regress to all other damage properties, such as size and angle. A loss function is designed to improve the optimization efficiency of network. Orientation-aware center point estimation network is applied to bolt damage detection, and comparison with the well-known Faster Region-Convolutional Neural Network (a benchmark using horizontal bounding box) demonstrates the accuracy of the proposed method. Finally, videos were utilized to verify the capability of the proposed orientation-aware center point estimation network in real-time detection of bolt damages.
      Citation: Structural Health Monitoring
      PubDate: 2021-03-29T10:02:39Z
      DOI: 10.1177/14759217211004243
       
  • Global–local model for three-dimensional guided wave scattering with
           application to rail flaw detection

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      Authors: Antonino Spada, Margherita Capriotti, Francesco Lanza di Scalea
      Abstract: Structural Health Monitoring, Ahead of Print.
      This study presents a three-dimensional global–local formulation for the prediction of guided wave scattering from discontinuities (e.g. defects). The approach chosen utilizes the Semi-Analytical Finite Element method for the “global” portion of the waveguide, and a full Finite Element discretization for the “local” portion of the waveguide containing the discontinuity. The application of interest is the study of guided wave scattering from transverse head defects in rails. Theoretical scattering results are impossible to obtain in this case for a wide-frequency range. While three-dimensional Semi-Analytical Finite Element–Finite Element models for guided wave scattering studies have been used in the past, this is the only study where guided waves in rails were modeled in a wide-frequency range (up to 180 kHz). A comparison analysis with a benchmark study of wave reflections from the free end of a cylindrical rod is conducted first. For the case of the rail, selected case studies of incoming guided modes were chosen, and reflection and transmission spectra are calculated for head defects of various sizes. This kind of results can be utilized to guide and/or interpret ultrasonic guided wave tests aimed at defect detection or quantification. Finally, parametric studies are conducted to examine more closely the role of certain operational parameters that are important in this kind of analysis, and specifically the size of the “local” region and the number of guided modes considered. These parametric studies lead to compromises that need to be struck on the basis of conservation of energy among all wave modes involved.
      Citation: Structural Health Monitoring
      PubDate: 2021-03-26T10:22:40Z
      DOI: 10.1177/14759217211000863
       
  • Damage assessment of a titanium skin adhesively bonded to carbon
           fiber–reinforced plastic omega stringers using acoustic emission

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      Authors: Milad Saeedifar, Mohamed Nasr Saleh, Peter Nijhuis, Sofia Teixeira de Freitas, Dimitrios Zarouchas
      Abstract: Structural Health Monitoring, Ahead of Print.
      This study is devoted to the use of acoustic emission technique for a comprehensive damage assessment, that is, damage detection, localization, and classification, of an aeronautical metal-to-composite bonded panel. The structure comprised a titanium panel adhesively bonded to carbon fiber–reinforced plastic omega stringers. The panel contained a small initial artificial debonding between the titanium panel and one of the carbon fiber–reinforced plastic stringers. The panel was subjected to a cyclic increasing in-plane compression load, including loading, unloading, and then reloading to a higher load level, until the final fracture. The generated acoustic emission signals were captured by the acoustic emission sensors, and digital image correlation was also used to obtain the strain field on the surface of the panel during the test. The results showed that acoustic emission can accurately detect the damage onset, localize it, and also trace its evolution. The acoustic emission results not only were consistent with the digital image correlation results, but also managed to detect the damage initiation earlier than digital image correlation. Finally, the acoustic emission signals were clustered using particle swarm optimization method to identify the different damage mechanisms. The results of this study demonstrate the capability of acoustic emission for the comprehensive damage characterization of aeronautical bi-material adhesively bonded structures.
      Citation: Structural Health Monitoring
      PubDate: 2021-03-25T08:44:13Z
      DOI: 10.1177/14759217211001752
       
  • Elimination of outlier measurements for damage detection of structures
           under changing environmental conditions

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      Authors: William Soo Lon Wah, Yining Xia
      Abstract: Structural Health Monitoring, Ahead of Print.
      Damage detection methods developed in the literature are affected by the presence of outlier measurements. These measurements can prevent small levels of damage to be detected. Therefore, a method to eliminate the effects of outlier measurements is proposed in this article. The method uses the difference in fits to examine how deleting an observation affects the predicted value of a model. This allows the observations that have a large influence on the model created, to be identified. These observations are the outlier measurements and they are eliminated from the database before the application of damage detection methods. Eliminating the outliers before the application of damage detection methods allows the normal procedures to detect damage, to be implemented. A multiple-regression-based damage detection method, which uses the natural frequencies as both the independent and dependent variables, is also developed in this article. A beam structure model and an experimental wooden bridge structure are analysed using the multiple-regression-based damage detection method with and without the application of the method proposed to eliminate the effects of outliers. The results obtained demonstrate that smaller levels of damage can be detected when the effects of outlier measurements are eliminated using the method proposed in this article.
      Citation: Structural Health Monitoring
      PubDate: 2021-03-24T09:33:44Z
      DOI: 10.1177/1475921721998476
       
  • Dynamic feature evaluation on streaming acoustic emission data for
           adhesively bonded joints for composite wind turbine blade

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      Authors: Dong Xu, Pengfei Liu, Zhiping Chen, Qimao Cai, Jianxing Leng
      Abstract: Structural Health Monitoring, Ahead of Print.
      Damage mode identification and premature failure prevention for composite structures by acoustic emission have drawn a great deal of attention. Feature evaluation on streaming acoustic emission data is one of the significant issues in research of acoustic emission signal processing. This work conducts dynamic feature evaluation on 15 conventional acoustic emission features so as to seek a deeper insight into different features with damage accumulation. First, the procedure of dynamic feature evaluation is presented based on three basic algorithms. Second, the streaming acoustic emission data are collected from the adhesively bonded composite single-lap joint subjected to quasi-static tensile loads. Third, further efforts are made so as to explore the information contained as well as to interpret the effect of damage accumulation. It is found that different conventional acoustic emission features show distinctive functions, including damage mode identification, damage process indication, and both of them. Informative features for damage pattern recognition are independent on damage accumulation. Useful features for damage process description show sensitive dynamic characteristics with damage accumulation, especially before the complete failure of the specimen. Furthermore, dynamic feature evaluation can be used to detect singular signals.
      Citation: Structural Health Monitoring
      PubDate: 2021-03-24T09:31:09Z
      DOI: 10.1177/14759217211001704
       
  • Online damage detection of earthquake-excited structure based on near
           real-time envelope extraction

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      Authors: Satyam Panda, Tapas Tripura, Budhaditya Hazra
      Abstract: Structural Health Monitoring, Ahead of Print.
      A robust real-time damage detection technique of earthquake-excited structures based on a new demodulation technique for nonlinear and non-stationary vibration signals through the identification of signal envelopes in real time is presented. In the present work, the need for the detection of envelope in a vibration signal in real time is addressed by reformulating the concept of Hermitian interpolation functions to a recursive Hermitian polynomial, which is a key entitlement of the present work. Once, the near real-time demodulation is achieved, the proposed framework proceeds to the newly developed error-adapted framework by addressing the errors accrued between the standard and generalized eigen perturbation formulation in the context of real-time estimation of proper orthogonal modes and linear normal modes. In the adaptive framework, the error is modeled as a feedback, which is constructed to account for the truncation in the order of eigen perturbation. In addition to the improved accuracy due to the envelope extraction, the proposed error-adapted eigen perturbation further improves the detectability over the currently available eigen perturbation–based real-time algorithms. To ensure robustness of the proposed algorithm, a new real-time damage indicator based on the maximum of principal eigenvector of the evolving transformed covariance matrix is proposed. The proposed modules together not only improve the detectability of the damage detection in real-time but also enhance the overall performance in presence of non-stationary excitation, that often mask the damage information in the higher energy zones of the amplitude and frequency-modulated response. Simulations for the proposed framework is performed by considering a 5 degrees-of-freedom linear and base-isolated nonlinear structural system driven by non-stationary stochastic excitations with damage simulated at intermediate floor at a particular time instant. Evidence of the near real-time demodulation and/or envelope removal from the signal and improved damage identification is also provided. An examination of the proposed framework using experimental data further validates the robustness of the proposed scheme.
      Citation: Structural Health Monitoring
      PubDate: 2021-03-23T07:22:10Z
      DOI: 10.1177/1475921721997068
       
  • Modified Gaussian convolutional deep belief network and infrared thermal
           imaging for intelligent fault diagnosis of rotor-bearing system under
           time-varying speeds

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      Authors: Li Xin, Shao Haidong, Jiang Hongkai, Xiang Jiawei
      Abstract: Structural Health Monitoring, Ahead of Print.
      The vast majority of the existing diagnostic studies using deep learning techniques for rotating machinery focus on the vibration analysis under steady rotating speed. Nevertheless, the collected vibration signals are sensitive to time-varying speeds and the vibration sensors may cause structure damage of equipment after long-term close contact. Aiming at these aforementioned problems, a modified Gaussian convolutional deep belief network driven by infrared thermal imaging is proposed to automatically diagnose different faults of rotor-bearing system under time-varying speeds. First, infrared thermal images are measured to characterize the working states of rotor-bearing system to reduce the impact of changeable speeds. Second, Gaussian units are used to construct Gaussian convolutional deep belief network to well deal with infrared thermal images. Finally, trackable learning rate is designed to modify the training algorithm to enhance the performance. The comparison results verify the feasibility of the proposed method, which outperforms the other methods.
      Citation: Structural Health Monitoring
      PubDate: 2021-03-20T06:24:54Z
      DOI: 10.1177/1475921721998957
       
  • Dual-frequency acousto-ultrasonic sensing of impact damage in composites
           for mitigating signal instability

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      Authors: Chen Gong, Qi Wu, Hanqi Zhang, Rong Wang, Ke Xiong, Yoji Okabe, Fengming Yu
      Abstract: Structural Health Monitoring, Ahead of Print.
      Signal instability due to temperature fluctuations, sensor degradation, and debonding introduces additional amplitude loss in the detected signals during acousto-ultrasonic detection, which may be falsely attributed to defects in a structure. First, we determined that the amplitudes of both high-frequency and low-frequency Lamb waves decrease after propagation through a damaged area. Then, we found that the amplitude ratio of such waves not only exhibits a downward trend but is also immune to fluctuations in the input signals. A qualitative numerical expression was proposed to explain this phenomenon, and preliminary experiments were conducted to demonstrate that the amplitude ratio is an effective parameter for mitigating instability in signal detection. Particularly, the number of impacts on a composite laminate was evaluated with respect to changes in the input signal amplitude. Notably, this method can be further simplified by designing a dual-frequency input signal. After conclusively validating the performance of the novel method in a composite subjected to temperature fluctuations, we conclude that the proposed acousto-ultrasonic detection method is robust in mitigating signal instability, and that it yields reliable information for damage evaluation.
      Citation: Structural Health Monitoring
      PubDate: 2021-03-19T10:01:37Z
      DOI: 10.1177/1475921721996625
       
  • A review of structural health monitoring of bonded structures using
           electromechanical impedance spectroscopy

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      Authors: A Francisco G Tenreiro, António M Lopes, Lucas FM da Silva
      Abstract: Structural Health Monitoring, Ahead of Print.
      The article presents a literature review of electromechanical impedance spectroscopy for structural health monitoring, with emphasis in adhesively bonded joints. The concept behind electromechanical impedance spectroscopy is to use variable high-frequency structural vibrations with piezoelectric elements to monitor the local area of a structure for changes in mechanical impedance that may indicate imminent damage. Various mathematical models that correlate the structural impedance with the electric response of the piezoelectric sensors are presented. Several algorithms and metrics are introduced to detect, localize, and characterize damage when using electromechanical impedance spectroscopy. Applications of electromechanical impedance spectroscopy to study adhesive joints are described. Research and development of alternative hardware for electromechanical impedance spectroscopy is presented. The article ends by presenting future prospects and research of electromechanical impedance spectroscopy–based structural health monitoring, and, while advances have been made in algorithms for damage detection, localization, and characterization, this technology is not mature enough for real-world applications.
      Citation: Structural Health Monitoring
      PubDate: 2021-03-17T09:22:05Z
      DOI: 10.1177/1475921721993419
       
  • A comprehensive evaluation method for concrete dam health state combined
           with gray-analytic hierarchy-optimization theory

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      Authors: Hao Gu, Meng Yang, Chongshi Gu, Zheng Fang, Xiaofei Huang
      Abstract: Structural Health Monitoring, Ahead of Print.
      Concrete dam health state has been taken seriously in the field of structural health monitoring. A limitation in the current research is a lack of clear physical significance and reasonable interval division for better reflecting the development trend of the health state. This article introduces a comprehensive evaluation method that provides information on the fusion of different monitoring types and influences of degree weight. The analysis method of variation tendency for concrete dam soundness is proposed based on gray theory. According to the principle of analytic hierarchy process, this article introduces an optimization division method of evaluation grade interval for concrete dam health state. Based on this, a method based on multiple monitoring quantities is established through studies of determination methods for monitoring various quantities of weight, which aimed at evaluating concrete dam health state and solving the problem of comprehensive evaluation for concrete dam health in an effective manner. An existing concrete dam is presented and discussed to serve as the validation of the established theory.
      Citation: Structural Health Monitoring
      PubDate: 2021-03-06T04:55:19Z
      DOI: 10.1177/1475921721993388
       
  • Life-cycle management cost analysis of transportation bridges equipped
           with seismic structural health monitoring systems

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      Authors: Michela Torti, Ilaria Venanzi, Simon Laflamme, Filippo Ubertini
      Abstract: Structural Health Monitoring, Ahead of Print.
      Life-cycle cost analysis is an approach that has gained popularity for assisting the design of civil infrastructures. The life-cycle cost analysis approach can be leveraged for structures equipped with structural health monitoring systems in order to quantify the benefits of the technology and de facto support its long-term implementation. However, for new structures, the long-term assessment of the expected value of the total investment cost, in terms of the current worth at the design time, is still the focus of ongoing research due to unknowns and uncertainties on the impact of the structural health monitoring system on long-term structural performance. This article proposes a new combined model of life-cycle cost formulation and simulation methodology for the long-term financial assessment of transportation bridges equipped with seismic structural health monitoring systems, in order to evaluate the total costs and benefits offered by such monitoring systems for post-seismic assessments. The formulation characterizes the time evolution of bridge management cost terms, highlighting the most sensitive parameters. The simulation methodology allows to quantitatively weigh each maintenance action on the total cost based on when the action is performed. The model is used to compare structures managed by the traditional approach of post-earthquake inspection versus those managed by a condition-based approach enabled by structural health monitoring systems. The originality of the model empowers the comparison by payback time, defined as the break-even point between costs and benefits of a structural health monitoring system, as well as by economic gain, defined as the difference between the total costs of an unmonitored versus a monitored structure through the end of service life. The proposed model is demonstrated through parametric analyses on a case study consisting of a continuous steel-concrete composite bridge, where the structural health monitoring system is used to monitor the elastic limit state condition of bending forces in piers during the earthquake.
      Citation: Structural Health Monitoring
      PubDate: 2021-03-04T09:14:26Z
      DOI: 10.1177/1475921721996624
       
  • Nonparametric nonlinear restoring force and excitation identification with
           Legendre polynomial model and data fusion

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      Authors: Bin Xu, Ye Zhao, Baichuan Deng, Yibang Du, Chen Wang, Hanbin Ge
      Abstract: Structural Health Monitoring, Ahead of Print.
      Identification of nonlinear restoring force and dynamic loadings provides critical information for post-event damage diagnosis of structures. Due to high complexity and individuality of structural nonlinearities, it is difficult to provide an exact parametric mathematical model in advance to describe the nonlinear behavior of a structural member or a substructure under strong dynamic loadings in practice. Moreover, external dynamic loading applied to an engineering structure is usually unknown and only acceleration responses at limited degrees of freedom of the structure are available for identification. In this study, a nonparametric nonlinear restoring force and excitation identification approach combining the Legendre polynomial model and extended Kalman filter with unknown input is proposed using limited acceleration measurements fused with limited displacement measurements. Then, the performance of the proposed approach is first illustrated via numerical simulation with multi-degree-of-freedom frame structures equipped with magnetorheological dampers mimicking nonlinearity under direct dynamic excitation or base excitation using noise-polluted measurements. Finally, a dynamic experimental study on a four-story steel frame model equipped with a magnetorheological damper is carried out and dynamic response measurement is employed to validate the effectiveness of the proposed method by comparing the identified dynamic responses, nonlinear restoring force, and excitation force with the test measurements. The convergence and the effect of initial estimation errors of structural parameters on the final identification results are investigated. The effect of data fusion on improving the identification accuracy is also investigated.
      Citation: Structural Health Monitoring
      PubDate: 2021-03-03T06:36:28Z
      DOI: 10.1177/1475921721994740
       
  • Evaluation tool for assessing the influence of structural health
           monitoring on decision-maker risk preferences

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      Authors: Antti Valkonen, Branko Glisic
      Abstract: Structural Health Monitoring, Ahead of Print.
      Different rationally behaving individuals can make different decisions even under same conditions. Concept of risk preference is one way of describing this discrepancy. Risk preference refers to the way decision-makers weight different risky options. Expected utility theory is a popular way to treat this weighting mathematically. Previous research in the field of structural health monitoring has shown that in a multi-stakeholder decision-making, discrepancies in risk preferences between the stakeholders can have large effects on the feasibility of structural health monitoring implementation. Results from that previous research obtained using expected utility theory showed that under certain conditions, differences in stakeholder risk attitudes can make the value of structural health monitoring system even negative. As the overall aim of structural health monitoring is to enable systematic data-based management of infrastructure, understanding of risk preferences and their effect on the structural health monitoring decision-making is of paramount importance. In this work, we created a risk preference assessment tool that is derived from Domain-Specific Risk-Taking scale. We propose the tool and explain its development and validation. The validation results indicate that the proposed assessment tool is technically sound and has high potential in the future valuation of human factors influencing decision-making based on structural health monitoring.
      Citation: Structural Health Monitoring
      PubDate: 2021-02-11T08:49:59Z
      DOI: 10.1177/1475921721992016
       
  • Predictive information and maintenance optimization based on decision
           theory: a case study considering a welded joint in an offshore wind
           turbine support structure

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      Authors: Muhammad Farhan, Ronald Schneider, Sebastian Thöns
      Abstract: Structural Health Monitoring, Ahead of Print.
      Predictive information and maintenance optimization for deteriorating structures is concerned with scheduling (a) the collection of information by inspection and monitoring and (b) maintenance actions such as repair, replacement, and retrofitting based on updated predictions of the future condition of the structural system. In this article, we consider the problem of jointly identifying—at the beginning of the service life—the optimal inspection time and repair strategy for a generic welded joint in a generic offshore wind turbine structure subject to fatigue. The optimization is performed based on different types of decision analyses including value of information analyses to quantify the expected service life cost encompassing inspection, repair, and fatigue damage for all relevant combinations of inspection time, repair method, and repair time. Based on the analysis of the expected service life cost, the optimal inspection time, repair method, and repair time are identified. Possible repair methods for a welded joint in an offshore environment include welding and grinding, for which detailed models are formulated and utilized to update the joint’s fatigue performance. The decision analyses reveal that an inspection should be scheduled approximately at mid-service life of the welded joint. A repair should be performed in the same year after an indication and measurement of a fatigue crack given an optimal inspection scheduling. This article concludes with a discussion on the results obtained from the decision and value of information analyses.
      Citation: Structural Health Monitoring
      PubDate: 2021-02-01T05:28:27Z
      DOI: 10.1177/1475921720981833
       
  • Quantifying the value of information from inspecting and monitoring
           engineering systems subject to gradual and shock deterioration

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      Authors: Leandro Iannacone, Pier Francesco Giordano, Paolo Gardoni, Maria Pina Limongelli
      Abstract: Structural Health Monitoring, Ahead of Print.
      The state of engineering systems changes in time due to the effect of gradual (e.g. corrosion, fatigue) and shock deterioration (e.g. earthquakes, floods, and tornados). At specified moments, for example, after a shock, decision-makers might wish to know the state of the system to take the optimal management action. Different data acquisition strategies such as inspections and continuous structural health monitoring (SHM) can help in the definition and prediction of the system state over time. The acquisition of information comes at a cost that must be balanced by the benefit it brings in terms of risk reduction. The value of information from Bayesian decision analysis quantifies the benefit provided by such information. This article proposes a formulation to compute the value of information of inspection and continuous SHM for degrading engineering systems. In the proposed formulation, the information collected before a given time is used to improve the prediction of the effects of gradual and shock deterioration processes and the future probability of failure. This article investigates the case study of a two-span reinforced concrete bridge degrading under the effect of chemical reactions and seismic actions.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-25T05:34:14Z
      DOI: 10.1177/1475921720981869
       
  • Improved current injection pattern for the detection of delaminations in
           carbon fiber reinforced polymer plates using electrical impedance
           tomography

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      Authors: Mathias Haingartner, Sandra Gschoßmann, Max Cichocki, Martin Schagerl
      Abstract: Structural Health Monitoring, Ahead of Print.
      In this article, we introduce a new current injection pattern for electrical impedance tomography. The pattern improves the quality of hole detection in carbon fiber reinforced polymer plates and allows the detection of delaminations. The new pattern is described in detail and compared to three widely used, classical injection patterns. The advantages of the new pattern are demonstrated by numerical finite element analyses for three test cases: a hole of 10-mm diameter, two simultaneous holes, and an ideal delamination in a circular region with a 50-mm diameter. The results are validated experimentally by comparing electrical impedance tomography measurements of a carbon fiber reinforced polymer plate with at first one, then two holes with a 10-mm diameter using classical patterns and the new pattern.
      Citation: Structural Health Monitoring
      PubDate: 2021-01-01T07:18:55Z
      DOI: 10.1177/1475921720972308
       
 
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