Abstract: The main motivation of the present study is to propose a new framework of multi-objective brain emotional learning-based intelligent controller (MOBELBIC) for tuning the command voltage of MR dampers in real-time for smart base-isolated structures. To address the main goal of the seismic control of such structures i.e. creating a suitable trade-off between the conflicting cost functions in terms of the maximum base displacement and superstructure acceleration, a multi-objective particle swarm optimization (MOPSO) algorithm is also utilized. Moreover, a multi-objective proportional–integral–derivative controller (MOPIDC) is proposed for comparison purposes. Then, the validation of both proposed controllers is compared with those given by the passive-off and passive-on statues of the MR damper for a benchmark base-isolated structure subjected to different earthquake excitations. Poor efficacy of the passive-off case is found especially for overcoming the drawbacks of large base displacement during near-field earthquakes. Besides, the passive-on case is significantly able to reduce the maximum and RMS values of the base displacement at the cost of a remarkable increase in the maximum and RMS values of the superstructure inter-story and acceleration, which shows that it cannot meet the main control objectives. The simulation result during different earthquake excitations indicates that the MOBELBIC performs much better than the MOPIDC in the simultaneous reduction of the maximum and RMS of the seismic responses of the studied structure especially in terms of base displacement, inter-story drift, and superstructure acceleration. PubDate: 2021-05-12

Abstract: Metaheuristic algorithms are successful methods of optimization. The firefly algorithm is one of the known metaheuristic algorithms used in a variety of applications. Recently, a new and efficient version of this algorithm was introduced as NEFA, which indicated a good performance in solving optimization problems. However, the introduced attraction model in this algorithm may not provide good coverage of the search space and thus trap the algorithm in a local optimum. In this paper, a new and efficient improved firefly algorithm called INEFA is proposed to improve the performance of NEFA. In INEFA, a new model of attraction is introduced in which each firefly can be attracted to brighter fireflies located in different areas of the search space, using the clustering concept to classify fireflies. To evaluate the performance of INEFA, it was used to optimize several known benchmark functions. The results were compared with the results of the firefly algorithm and some of its known improvements. The comparison of results indicated the significant power of INEFA compared to the algorithms. It was used to evaluate its application in solving a constrained optimization problem. The comparison results showed that INEFA performs better than most of the compared algorithms. PubDate: 2021-05-12

Abstract: In the current paper, vibrational and critical circular speed characteristics of a functionally graded (FG) rotary microdisk is examined considering a continuum nonlocal model called modified couple stress (MCS) model, for the first time in the literature. The generalized differential quadrature (GDQ) approach and variational method are used for deriving and solving the non-classical final relations. The FG size-dependent micro-sized disk’s final relations and corresponding boundary conditions (BCs) are achieved on the basis of the higher-order shear deformation (HSD) model. Then, a parametric analysis has been conducted to analyze the influences of the length scale factor, circumferential, radius ratio and radial mode number, FG material’s configuration, and BCs on the FG micro-scaled disk’s frequency by taking into account the MCST. The outcomes reveal that, at the initial value of the FG index (β), the negative impact from rotating speed on the dynamic stability of the system becomes bold. Furthermore, at the β factor’s lower amount and spinning velocity’s higher amount, there is instability in the responses of the system. Additionally, it is indicated that the negative effect from radius ratio on the frequency responses of the rotary FG microdisk becomes considerable at the length scale factor’s higher amount. PubDate: 2021-05-12

Abstract: The vibration and damping characteristics of carbon nanotubes reinforced (CNTR) skewed shell structure under a hygrothermal environment have been investigated using the finite element method. CNT as reinforcing phase and polymer as matrix phase are considered for the nanocomposites (NCs) based viscoelastic skewed shell structure. Dynamic mechanical analysis is used to conduct the creep test for NCs samples which were fabricated, as per ASTM-D4065 standard, and obtained the viscoelastic properties in the frequency domain under different hygrothermal conditions. The shell geometry is defined by considering an arbitrary coordinate system for the skewed shell structure. Finite element modelling has been done with Serendipity element with five degrees of freedom in all eight nodes. The present formulation is based on Koiter’s shell theory and first-order shear deformation theory is considered to incorporate the transverse shear effect based on Mindlin’s hypothesis. The frequency dependant viscoelastic properties are directly used to obtain the frequency responses of the skewed shell panel using fast Fourier transform (FFT) whereas the transient responses are determined using inverse fast Fourier transform (IFFT). An in-house MATLAB code is developed for the numerical simulation and the accuracy of the proposed formulation is validated with available results in literatures and using ANSYS software. A parametric study has been carried out for the skewing angle and CNT volume fraction on the vibration behaviour of different thin and thick NC skewed shell structures under various hygrothermal conditions. PubDate: 2021-05-12

Abstract: Boundary representation (B-rep) model editing plays an essential role in computer-aided design, and has motivated the very recent direct modeling paradigm, which features intuitive push–pull manipulation of the model geometry. In mechanical design, a substantial part of B-rep models being used are quadric models (composed of linear and quadric surfaces). However, push-pulling such models is not trivial due to the possible smooth face–face connections in the models. The major issue is that, during push–pull moves, it is often desirable to preserve these connections for functional, manufacturing, or aesthetic reasons, but this could cause complex inconsistencies between the geometry and topology in the model and lead to robustness issues in updating the model. The challenge lies in effectiveness towards detecting the instants when geometry–topology inconsistencies occur during push–pull moves. This paper proposes a novel reverse detection method to solve the challenge and then, based on it, presents a robust method for push–pull direct modeling while preserving smooth connections. Case studies and comparisons have been conducted to demonstrate the effectiveness of the method. PubDate: 2021-05-12

Abstract: The study is investigated the capacity of new artificial intelligence (AI) methodologies for shear strength (Vs) computation of reinforced concrete (RC) beams. The development of extreme gradient boosting (XGBoost) and multivariate adaptive regression splines (MARS) models as a robust AI methodology are tested for Vs prediction. The proposed models are developed based on collected experimental data from the literature including the beam geometric and concrete properties parameters. There are nine input combinations adopted based on the associated input parameters for the predictive models construction. Support vector machine (SVM) model is conducted for validation purpose. In addition, several empirical formulations are recalled from the literature for comparison. Research findings evidenced the potential of the proposed XGBoost and MARS models for modeling the Vs reinforced concrete beams. The modeling accuracy performance comparison with the established AI models and the empirical formulas confirmed the capacity of the proposed models. Results indicated that all the utilized beam geometric and concrete properties parameters are significant for the predictive model development. In quantitative terms, MARS model attained minimum root mean square error (RMSE = 89.96 KN). In general, the research provided a reliable and robust soft computing model for Vs reinforced concrete beams computation that contribute to the basic knowledge of structural engineering design and sustainability. PubDate: 2021-05-11

Abstract: We present an \(hr\) -adaptivity framework for optimization of high-order meshes. This work extends the r-adaptivity method by Dobrev et al. (Comput Fluids, 2020), where we utilized the Target-Matrix Optimization Paradigm (TMOP) to minimize a functional that depends on each element’s current and target geometric parameters: element aspect-ratio, size, skew, and rotation. Since fixed mesh topology limits the ability to achieve the target size and aspect-ratio at each position, in this paper, we augment the r-adaptivity framework with nonconforming adaptive mesh refinement to further reduce the error with respect to the target geometric parameters. The proposed formulation, referred to as \(hr\) -adaptivity, introduces TMOP-based quality estimators to satisfy the aspect-ratio target via anisotropic refinements and size target via isotropic refinements in each element of the mesh. The methodology presented is purely algebraic, extends to both simplices and hexahedra/quadrilaterals of any order, and supports nonconforming isotropic and anisotropic refinements in 2D and 3D. Using a problem with a known exact solution, we demonstrate the effectiveness of \(hr\) -adaptivity over both r- and \(h\) -adaptivity in obtaining similar accuracy in the solution with significantly fewer mesh nodes. We also present several examples that show that \(hr\) -adaptivity can help satisfy geometric targets even when \(r\) -adaptivity fails to do so, due to the topology of the initial mesh. PubDate: 2021-05-05

Abstract: The blood flow with heat transportation has prominent clinical importance during the levels where the blood flow needs to be checked (surgery) and the heat transportation rate must be controlled (therapy). This work presents an analysis of the melting heat transport of blood, which consists of iron nanoparticles along free convection with cross-model and solution of the partial differential equation (PDEs) are emerged by the mathematical model. Being the importance of iron oxide nanoparticles in applications of the biomedical field due to their intrinsic properties such as colloidal stability, surface engineering capability and low toxicity, this study has been launched. Furthermore, PDEs of the problem are converted into a set of nonlinear ordinary differential equations (ODEs) by proper transformations. The solution of this system of ODEs is calculated through RK 4 method and Keller–Box scheme. Some leading points and numerical results of this study of both types of presence and absence of meting effects are tabulated. PubDate: 2021-04-30

Abstract: Variable-fidelity surrogate-based efficient global optimization (EGO) method with the ability to adaptively select low-fidelity (LF) and high-fidelity (HF) infill point has emerged as an alternative to further save the computational cost of the single-fidelity EGO method. However, in terms of the variable-fidelity surrogate-assisted multi-objective optimization methods, existing methods rely on empirical parameters or are unable to adaptively select LF/HF sample in the optimal search process. In this paper, two variable-fidelity hypervolume-based expected improvement criteria with analytic expressions for variable-fidelity multi-objective EGO method are proposed. The first criterion relies on the concept of variable-fidelity expected improvement matrix (VFEIM) and is obtained by aggregating the VFEIM using a simplified hypervolume-based aggregation scheme. The second criterion termed as VFEMHVI is derived analytically based on a modified hypervolume definition. Both criteria can adaptively select new LF/HF samples in the iterative optimal search process to update the variable-fidelity models towards the HF Pareto front, distinguishing the proposed methods to the rests in the open literature. The constrained versions of the two criteria are also derived for problems with constraints. The effectiveness and efficiency of the proposed methods are verified and validated over analytic problems and demonstrated by two engineering problems including aerodynamic shape optimizations of the NACA0012 and RAE2822 airfoils. The results show that the VFEMHVI combined with the normalization-based strategy to define the reference point is the most efficient one over the compared methods. PubDate: 2021-04-29

Abstract: Via the nonlocal stress–strain gradient continuum mechanics, the microscale-dependent linear and nonlinear large deflections of transversely loaded composite sector microplates with different thickness variation schemes are investigated. Microplates are assumed to be prepared from functionally graded materials (FGMs) the characteristics of which are changed along the thickness direction. A quasi-3D plate theory with a sinusoidal transverse shear function in conjunction with a trigonometric normal function was employed for the establishment of size-dependent modelling of FGM microplates with different thickness variation schemes. Then, to solve the nonlocal stress–strain gradient flexural problem, the non-uniform rational B-spline type of isogeometric solution methodology was applied for an accurate integration of geometric discerptions. It was found that the gap between load–deflection curves drawn for linear, concave and convex thickness variation patterns became greater by changing FGM composite microplate boundary conditions from clamped to simply supported. In addition, it was found that by considering only the nonlocal size effect, the plate deflection obtained by the nonlocal strain gradient quasi-3D plate model was greater than that extracted by the classical continuum elasticity because of the softening character of nonlocal size effect, while the strain gradient microstructural size dependency acted in opposite way and represented a stiffening character. PubDate: 2021-04-29

Abstract: Over the past few decades, it has been observed a remarkable progression in the development of computer aid models in the field of civil engineering. Machine learning models provide a reliable and robust alternative modeling design for solving complex engineering issues. The current study introduced three versions of newly explored ensemble machine learning models [extreme gradient boosting (XGBoost), multivariate adaptive regression spline (MARS) and random forest (RF)] for fiber-reinforced polymer (FRP) composite strain prediction. An experimental dataset were collected from the literature with total number of 729 experiments. The dataset is presented the FRP strain and its influential parameters including material geometry, strength properties, strain properties, FRP properties, and confinement properties. Five different input combination were built for the prediction process of the FRP composite strain. The current research results were validated against the well-established literature review empirical formulations and the machine learning models. In general, the modeling results confirmed the capacity of the proposed new ML models in predicting the strain enhancement ratio. The fifth input combination incorporated all the predators attained the best modeling accuracy results. However, the developed MARS model could achieve acceptable and superior prediction results using only strain properties parameters. Overall, the research finding confirmed the significant of the proposed ensemble ML as reliable alternative computer aid model for solving strain enhancement ratio problem. PubDate: 2021-04-28

Abstract: This paper is devoted to numerical investigations on mechanical behavior of cracked composite functionally graded (FG) plates. We thus develop an efficient adaptive approach in terms of the extended isogeometric analysis (XIGA) enhanced by locally refined non-uniform rational B-spline (LR NURBS) for natural frequency and buckling analysis of cracked FG Mindlin–Reissner plates. In this setting, the crack geometries, which are described by the level sets, are independent of the computational mesh; and the LR NURBS basis functions, which have the abilities of local refinement and modeling the complex shape, are used as the shape functions of XIGA. According to the recovered stresses of the first buckling or free vibration mode, a posteriori error estimator for driving the adaptive process is defined. The accuracy can be effectively improved through adaptive local refinements, demonstrated through numerical experiments with complex geometries. Compared with uniform global refinement, XIGA based on adaptive local refinement has the characteristics of high precision at low cost and fast convergence rate. The effects of some factors such as crack length and location, plate thickness, gradient index, boundary condition, loading type, etc. on critical buckling loads and natural frequencies are investigated. PubDate: 2021-04-28

Abstract: Failure-probability-based global sensitivity (FP-GS) analysis can measure the effect of the input uncertainty on the failure probability. The state-of-the-art for estimating the FP-GS are less efficient for the rare failure event and the implicit performance function case. Thus, an adaptive Kriging nested Importance Sampling (AK-IS) method is proposed in this work to efficiently estimate the FP-GS. For eliminating the dimensionality dependence in the calculation, an equivalent form of the FP-GS transformed by the Bayes’ formula is employed by the proposed method. Then the AK model is nested into IS for recognizing the failure samples. After all the failure samples are correctly identified from the IS sample pool, the failure samples are transformed into those subjected to the original conditional probability density function (PDF) on the failure domain by the Metropolis–Hastings algorithm, on which the conditional PDF of the input on the failure domain can be estimated for the FP-GS finally. The proposed method highly improves the efficiency of estimating the FP-GS comparing with the state-of-the-art, which is illustrated by the results of several examples in this paper. PubDate: 2021-04-27

Abstract: Liquefaction has caused many catastrophes during earthquakes in the past. When an earthquake is occurring, saturated granular soils may be subjected to the liquefaction phenomenon that can result in significant hazards. Therefore, a valid and reliable prediction of soil liquefaction potential is of high importance, especially when designing civil engineering projects. This study developed the least squares support vector machine (LSSVM) and radial basis function neural network (RBFNN) in combination with the optimization algorithms, i.e., the grey wolves optimization (GWO), differential evolution (DE), and genetic algorithm (GA) to predict the soil liquefaction potential. Afterwards, statistical scores such as root mean square error were applied to evaluate the developed models. The computational results showed that the proposed RBFNN-GWO and LSSVM-GWO, with Coefficient of Determination (R2) = 1 and Root Mean Square Error (RMSE) = 0, produced better results than other models proposed previously in the literature for the prediction of the soil liquefaction potential. It is an efficient and effective alternative for the soil liquefaction potential prediction. Furthermore, the results of this study confirmed the effectiveness of the GWO algorithm in training the RBFNN and LSSVM models. According to sensitivity analysis results, the cyclic stress ratio was also found as the most effective parameter on the soil liquefaction in the studied case. PubDate: 2021-04-27

Abstract: This paper presents two distinct model updating strategies for dynamical systems with local nonlinearities based on acceleration time history responses measured spatially across the vibrating structure. Both linear and nonlinear parameters are calibrated by minimizing the selected metric based on measured and predicted response using the newly proposed variant of differential search algorithm named as multi-cluster hybrid adaptive differential search (MCHADS) algorithm. The first model updating strategy involves the decoupling of linear and nonlinear characteristics of the system. In this scheme, we first establish the dynamic stiffness matrix using input and output measurements and then the underlying linear system is alone updated using it. Later, localization of the nonlinear attachment is attempted using the inverse property of the FRF of the nonlinear system and the established dynamic stiffness matrix of the underlying linear system from the previous step. Once the linear system is updated and with the already identified location(s) of nonlinear attachment(s), the nonlinear parameters are identified by formulating it as an optimization problem using the proposed MCHADS algorithm. The major advantage of the first approach is the reduction of the complex problem of nonlinear model updating to linear model updating. The second approach involves updating both linear and nonlinear parameters simultaneously using the proposed MCHADS algorithm. Investigations have been carried out by solving several numerically simulated examples and also with the experimental data of a benchmark problem to evaluate the effectiveness of the two proposed nonlinear model updating strategies. Further, investigations are also carried out to evaluate the capability of the model updating approaches towards damage identification in initially healthy nonlinear systems. Conclusions are drawn based on these investigations, highlighting the strengths and weaknesses of the two model updating approaches PubDate: 2021-04-26

Abstract: Plate structures are the integral parts of any maritime engineering platform. With the recent focus on composite structures, the need for optimizing their design and functionality has now been tremendously realized. In this paper, a comprehensive study is carried out on the effectiveness and optimization performance of three metaheuristic algorithms in designing skew composite laminates under dynamic operational environments. The natural frequencies of the composite panels are measured using a first-order shear deformation theory-based finite element (FE) approach. The stacking sequence of the composite panels is optimized so that the natural frequency separation between the first two modes is maximized. The three metaheuristics considered here are genetic algorithm (GA), repulsive particle swarm optimization with local search and chaotic perturbation (RPSOLC), and co-evolutionary host-parasite (CHP) algorithm. It is observed that in general, the FE-coupled metaheuristic algorithms are quite capable to significantly improve the baseline designs. In particular, FE-CHP algorithm outperforms both the FE-GA and FE-RPSOLC algorithms with respect to accuracy, computational speed and solution reliability. PubDate: 2021-04-24

Abstract: Recent work trend is to hybridize two and more variants in order to recognize better quality of functional and remedies to the global challenges of optimisation. The newly formed Slime Mould Algorithm (SMA) is premised upon naturally occurring slime mould oscillation feature. There is an effort to build a more effective way to accomplish exploration through process of exploitation. In a comprehensive collection of tests, the proposed hybrid Slime Mould Algorithm (SMA) – Pattern Search Algorithm (PS) (hSMA-PS) has been evaluated by comparing against accurate and reliable meta-heuristics for accuracy testing. In addition, nine classical engineering based optimization problems with design are used to guesstimate the algorithm’s efficacy in engineering based optimization challenges. The experiments demonstrate that the suggested algorithm enjoys efficiency, sometimes amazing result on sophisticated search landscapes. PubDate: 2021-04-23

Abstract: The safety and health monitoring of dams has attracted increasing attention. In this paper, a novel prediction model based on variational autoencoder (VAE) and temporal attention-based long short-term memory (TALSTM) network is proposed for the long-term deformation of arch dams. In the proposed model, the convolutional neural network-based VAE is applied to extracting the features of environmental data. The TALSTM is employed to construct the relationship between the dam displacement and extracted features. For verification, an arch dam is taken as an example. Through the comparison among nine baseline prediction models, the proposed model is more stable and effective than other prediction models. Furthermore, the proposed model could capture the features of environmental data accurately and provide better prediction results. Therefore, the proposed model is more suitable for engineering applications. PubDate: 2021-04-22

Abstract: Accurate prediction of ground vibration caused by blasting has always been a significant issue in the mining industry. Ground vibration caused by blasting is a harmful phenomenon to nearby buildings and should be prevented. In this regard, a new intelligent method for predicting peak particle velocity (PPV) induced by blasting had been developed. Accordingly, 150 sets of data composed of thirteen uncontrollable and controllable indicators are selected as input dependent variables, and the measured PPV is used as the output target for characterizing blast-induced ground vibration. Also, in order to enhance its predictive accuracy, the gray wolf optimization (GWO), whale optimization algorithm (WOA) and Bayesian optimization algorithm (BO) are applied to fine-tune the hyper-parameters of the extreme gradient boosting (XGBoost) model. According to the root mean squared error (RMSE), determination coefficient (R2), the variance accounted for (VAF), and mean absolute error (MAE), the hybrid models GWO-XGBoost, WOA-XGBoost, and BO-XGBoost were verified. Additionally, XGBoost, CatBoost (CatB), Random Forest, and gradient boosting regression (GBR) were also considered and used to compare the multiple hybrid-XGBoost models that have been developed. The values of RMSE, R2, VAF, and MAE obtained from WOA-XGBoost, GWO-XGBoost, and BO-XGBoost models were equal to (3.0538, 0.9757, 97.68, 2.5032), (3.0954, 0.9751, 97.62, 2.5189), and (3.2409, 0.9727, 97.65, 2.5867), respectively. Findings reveal that compared with other machine learning models, the proposed WOA-XGBoost became the most reliable model. These three optimized hybrid models are superior to the GBR model, CatB model, Random Forest model, and the XGBoost model, confirming the ability of the meta-heuristic algorithm to enhance the performance of the PPV model, which can be helpful for mine planners and engineers using advanced supervised machine learning with metaheuristic algorithms for predicting ground vibration caused by explosions. PubDate: 2021-04-22

Abstract: In this study, a new fractal-fractional (FF) derivative is defined by coupling the local conformable derivative and non-local Caputo fractional derivative. Using the defined derivative, a space–time FF version of the modified Benjamin–Bona–Mahony type equations is introduced. A collocation technique based on the orthonormal Bernoulli polynomials and their derivative matrices (including the ordinary and FF derivative matrices obtained in this study) is adopted for solving such equations. The presented method converts solving this equation to solve a simple system of algebraic equations. Some numerical problems are provided to show the accuracy of the expressed scheme. PubDate: 2021-04-20