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Abstract: Abstract Utility Grid outages may affect the industrial power system load-generation balance and cause rapid frequency decays particularly due to low inertia and small spinning reserve. Frequency stability is an essential perspective for the invulnerable operation of islanded industrial power systems with in-plant generation. Multi-machine system frequency response (MMSFR) model with the dynamic of governors, overall inertia of the system (generators and loads), and load damping is considered to find the approximate value of load shedding (LS) amount. The frequency variation of the islanded system is evaluated for various operating scenarios with a calculated approximate LS amount to determine the optimum amount of LS. In this paper, an artificial neural network (ANN)-based optimum and adaptive LS technique have been presented. The total in-plant generation, spinning reserve, total power import, total demand, and frequency decay rate have been selected as the input neurons of the ANN. The regrouping particle swarm optimization (RegPSO) algorithm has been adopted to obtain more accurate and faster training of the ANN. The oil refinery power distribution system with in-plant generation is considered to evaluate the performance of the proposed RegPSO-ANN load shedding algorithm. The performance of the proposed scheme is evaluated in comparison with the under-frequency relay-based conventional load shedding scheme and Levenberg–Marquardt back-propagation ANN-based LS scheme. PubDate: 2023-03-16
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Abstract: Abstract In digital era, image can be easily forged by multiple manipulations using advance editing tools, such that truthfulness of that image cannot be identified by human eye. Many approaches have been proposed for the detection of these forged images. However, the performance of these approaches is quite better for large resolution and uncompressed images, whereas they fail for small-sized highly compressed images. To address this issue, a novel DCT-3DCNN architecture is proposed for multiple manipulation detection. The proposed DCT-3DCNN is constructed by stacking the DCTs of four residuals (Average filtering residuals, Gaussian filtering residuals, Laplacian filtering residuals and median filtering residuals) along depth-wise. The four DCTs are more capable to extract the manipulations traces in an image. These traces are fed into 3D-CNN to learn the low to high level features of multiple manipulations. Thus, the features are combined to classify the forged and pristine images. The performance of the proposed DCT-3DCNN is supported by exhaustive experiments for binary classification and multi- class classifications. Experiments are conducted on five (UCID, RAISE, BOSSBase, BOWS2 and NRCS) databases. The robustness of the proposed network is also evaluated for the detection of bilateral filtering on images. For binary classification, the improvement ratio (%) between the proposed (DCT-3DCNN) and state-of-the-art methods (MFR-CNN, RF-CNN) is 4–5%, while for bilateral filtering the improvement ratio (%) is 8% in comparison with the state-of-the art method RF-CNN. The proposed network achieves 14% improvement in detection accuracy for multi-class classification as compared to the RF-CNN. PubDate: 2023-03-16
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Abstract: Abstract This paper presents a novel modal contribution-based damage identification system for low- to medium-rise buildings with simple and regular geometries using multi-step ambient vibration tests (AVT). The algorithm uses a multi-damage sensitivity feature and MATLAB programming to enable vibration-based structural health monitoring (SHM) of structures. To monitor changes in the data received from accelerometers attached to the structure, the system employs classical and stochastic data processing techniques and frequency-domain decomposition (FDD)-based modal identification. The proposed system uses a Modal Contributing Parameter (MCP) for damage identification and is validated using experimentation and finite element analysis (FEA) on two structures: a three-story steel shear frame and a two-story reinforced concrete (RC) frame building scaled down to 1:6. The study includes various single- and multiple-damage cases for the steel shear frame model, and the proposed algorithm successfully detects the induced damages. Additionally, the shake table tests on the two-story RC frame building show that the proposed algorithm can accurately detect damage and locate the damaged story(s). Overall, the results demonstrate the effectiveness of the proposed modal contribution-based damage identification system for condition monitoring and damage detection of building structures. PubDate: 2023-03-16
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Abstract: Abstract The removal of fog or haze from video frames and images has been a major focus in the area of computer vision since fog has a detrimental effect on monitoring and surveillance systems, as well as on the recognition of scene objects and other applications. Numerous defogging strategies have been presented thus far, including those based on the “colour-line model”, polarization, “anisotropic diffusion”, and the “dark channel prior” (DCP). Nevertheless, when the scene counters a thick fog and sky regions, these approaches fail to provide high-quality output. The authors suggest a novel haze/fog removal approach that uses tetrolet transformation to decompose a foggy image into low- and high-frequency components based on their structural information and dual dictionary learning-based residual frequency extractor to extract additional residual image information. DCP operation is performed on the low-frequency component to recover more fog-free information while sharpening the tetrolet coefficients extracts finer details. The inverse transformed image is then added to the residual high-frequency image component and post-processed using contrast limited adaptive histogram equalization to balance the contrast. Lastly, S and V channel gain regulator optimizes the contrast-enhanced image's colour and intensity. Compared to current methodologies, the suggested method significantly improves the overall picture quality. Quantitative and qualitative data support the statements. PubDate: 2023-03-16
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Abstract: Abstract In this work, a quad-port fractal MIMO antenna is configured, simulated, and experimented for ultra-wideband performance with the elimination of three interfering narrow bands. The antenna is assembled on dual 80 × 99.4mm2 FR-4 dielectric substrates wherein the top substrate comprises four 2nd iterative Sierpinski gasket fractal patches and the bottom substrate incorporates a feeding arrangement. The aperture-coupled feeding approach is employed to activate the fractal radiators. The multi-frequency response produced by fractal antenna elements is transformed into a wide working range by modifying apertures (clipped from the ground surface) into spiral-shaped structures and offsetting the feedlines toward the right. The designed array effectively radiates in the 3.07–11 GHz range with 112.7% fractional bandwidth while maintaining minimal inter-element isolation of 16.19 dB. Additionally, each feedline is amalgamated with circular split-ring resonators, U-slots, and rectangular split-ring resonators to eradicate the interference emerging from the C-band (downlink satellite), WLAN, and radio-location band, respectively. Several diversity operation attributes are analyzed and are found to inhere to their admissible standards. The measured performance of the fabricated antenna design (with and without band-stop structures) depicts an agreeable similitude with the simulation results, thus validating the real-world operability of the proposed MIMO antenna in ultra-wideband communication devices. PubDate: 2023-03-15
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Abstract: Abstract The outcomes of partial slip on double-diffusive convection of Johnson–Segalman nanofluids in an asymmetric peristaltic path are presented in this research with the effect of inclined magnetic field. The mathematical formulation of Johnson–Segalman nanofluids is also discussed with double-diffusive convection and inclined magnetic field. To simplify extremely nonlinear partial differential equations, a lubricant approach is applied. The numerical calculations are obtained to the equations for the stream function, concentration, pressure gradient, temperature, velocity, nanoparticle volume fraction, and pressure rise. The impact of prominent hydro-mechanical parameters such as Brownian motion, thermophoresis, Soret, Dufour, and slip constraints on the axial velocity, trapping, volumetric fraction, pressure gradient, temperature, pressure rise, and concentration functions is evaluated graphically. It is noted that slip effect in the channel causes the fluid particles to stray, slowing the fluid velocity. Moreover, it has been noted that as thermophoretic effects and Brownian motion increase, nanoparticles rapidly move from the wall into the fluid, significantly raising temperature. PubDate: 2023-03-14
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Abstract: Abstract The pursuit of an effective exit hole closure method for preparing high-quality welds has gained significant attention in the research community, driven by the potential of friction stir welding in space and aircraft manufacturing. Many techniques have been developed for avoiding the defect, but no effective solution has yet been observed. In this study, the potential of induction heating was investigated as an alternative method to repair exit holes. The present work aims to find an optimal set of induction heating parameters to effectively fuse a billet of similar volume to an exit hole. A numerical investigation was conducted by developing a two-dimensional (2D) axis-symmetric coupled electromagnetic-thermal model to analyze the effect of induction heating process parameters on the thermal behavior of AA1100 aluminum alloy. Process parameters such as coil geometry, stand-off distance, current and excitation frequency were varied, and the optimal parameters were identified based on temperature distribution, heating rate, thermal stress and phase change in the material. A pancake coil geometry, stand-off distance of 2 mm, current density of 1000 A and frequency of 100 kHz were the optimal set of parameters chosen from the numerical analysis for the effective repair of exit hole. A preliminary experimental investigation was conducted by employing the chosen process parameters and a powder-flux mixture in place of the billet. The observations reveal the possibility of achieving a sound joint under solid-state sintering condition. The use of fine powder-flux mixture at high temperatures facilitated solid-state diffusion resulting in a defect-free interface. The proposed system can be an alternative to the existing processes used for repair with an advantage to act as an in situ repair technique without compromising the original weld properties in the vicinity of the defect. The findings of this study have significant implications for space and aircraft manufacturing, where the use of friction stir welding is prevalent and exit hole defects are a challenge. PubDate: 2023-03-14
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Abstract: Abstract Structural Health Monitoring (SHM) based on electromechanical impedance (EMI) has been widely used in different engineering domains for the detection of structural damages, especially for detecting cracks that happen due to multiple reasons such as various natural conditions and operating cycles. However, studies of SHM based on EMI technique for the detection of faults in photovoltaic (PV) solar cells are very limited. This study aims to develop and integrate the EMI technique as a permanent monitoring system, to detect structural faults in advance, to maintain the PV system’s effectiveness and to ensure safety from catastrophic accidents. This work presents a numerical analysis of different models, such as free piezoelectric PZT patches of various shapes and several scenarios for healthy and cracked solar cells in order to investigate the capabilities of this technique. The crack is highlighted as a common damage in PV solar cells, and two of its characteristics were investigated namely, the crack location and the crack depth, where both are simulated with a pseudo-square monocrystalline solar cell. In addition, the root mean square deviation damage index is used to assess the severity of the damage. The results indicate that the damage index frequently changes with the variation in location and depth of the crack. Indeed, the proposed EMI technique can efficiently estimate the damage and its severity, which makes it possible to integrate it as a permanent detection and monitoring technique in a PV system. PubDate: 2023-03-14
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Abstract: Abstract The proportion of traditional frequency regulation units decreases as renewable energy increases, posing new challenges to the frequency stability of the power system. The energy storage of base station has the potential to promote frequency stability as the construction of the 5G base station accelerates. This paper proposes a control strategy for flexibly participating in power system frequency regulation using the energy storage of 5G base station. Firstly, the potential ability of energy storage in base station is analyzed from the structure and energy flow. Then, the framework of 5G base station participating in power system frequency regulation is constructed, and the specific steps are described. Finally, with the objective to minimize the power vacancy, the optimization model of the 5G base station auxiliary power system frequency response is established. Considering two cases of power system frequency rises and drops, the response proportion of base station in different operating states is solved from the proposed model. The simulation results show that the proposed method can not only help the system frequency recover quickly, but also reduce overshoot when the frequency fluctuates of power system. PubDate: 2023-03-13
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Abstract: Abstract The present study integrates multidisciplinary geological and petrophysical approaches to characterize and evaluate the potential of the pre-Cenomanian Nubian sandstone reservoirs in the Ramadan oil field, the central offshore part of the Gulf of Suez, Egypt. The different petrophysical parameters of the Nubian sandstone reservoirs (shale volume, porosity, water saturation as well as gross and net-pay thicknesses) were mapped, and 3D slicing models for the hydrocarbon phases saturation were constructed to understand the reservoir heterogeneity and the distribution of the best reservoir facies. The petrophysical results of the pre-Cenomanian Nubian succession highlight very good reservoir intervals in the Nubian C sandstones containing thick pay zones (> 120 m). On the other side, the Nubian A and B reservoir rocks are less prospective with pay zone horizons (< 10 m). Integrated reservoir models and wireline log analysis elucidate that clay volume is the most detrimental factor to the reservoir quality as the pay zone thickness and hydrocarbon saturation often increase where the clay volume decreases. Therefore, the presence of scattered pay zone intervals in Nubian A and B is mainly related to their elevated clay content which acts as barriers for fluids flowing within the reservoir facies. The Nubian C succession contains three different reservoir rock types (RRT) with variable compositional and petrophysical properties. RRTI and RRTII sandstones comprise quartzose sandstones with very low clay content (< 10%) and are characterized by an open pore system dominated by macropores. These sandstones are less impacted by overburden pressure and therefore can preserve their depositional porosity and permeability. On the other hand, RRTIII reservoir rocks are clay rich (> 10%) with abundant mesopores that are more prone to compressibility and hence reduction of the pore volume and pore throat. The present study highlights the significance of comprehensive integration between wireline logs, cores, and 3D reservoir models in directing exploration endeavors toward prospective reservoirs in mature basins. PubDate: 2023-03-12
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Abstract: Abstract Semantic segmentation acts as a major role in classifying the remote sensing images into oceanic ice, vegetation, roads, vehicles, houses, and more for offering high precision at the pixel level. In recent studies, convolutional neural network (CNN) has accomplished superior efficiency in the semantic segmentation of images. Even though various deep techniques and architectures have been utilized for enhancing the accuracy, it suffers from classifying the confused classes. Due to the optical conditions and remote sensing information, the sub-decimeter aerial imagery segmentation is challenging while achieving fine-grained semantic segmentation outcomes. The core goal of this task is to adopt the latest Adaptive DeepLabv3 strategy for enhanced semantic segmentation of aerial images. In Adaptive Deeplabv3, the involvement of both the encoder-decoder structure and spatial pyramid pooling module with adaptiveness by a hybrid meta-heuristic algorithm makes faster and stronger segmentation performance within less search space and reduced computation time. The relevant parameters of DeepLabv3 are tuned or optimized by the hybrid meta-heuristic algorithm based on Genetic Inspired Red Deer Algorithm (G-RDA). The enhanced segmentation is employed concerning a fitness function with precision and accuracy. Finally, the experimental analysis of the suggested Adaptive DeepLabv3 strategy for semantic segmentation of aerial images proves its competitive solution when evaluated over conventional approaches. PubDate: 2023-03-11
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Abstract: Abstract The behavior of a reinforced concrete beam under a dynamic load, i.e., an impact load, affects the overall structural system by altering its static and dynamic features. This study investigates the performance of large-scale reinforced concrete (RC) beams under different impact loads and the residual load-bearing and displacement capacities of impact-damaged beams through static loading tests. Identical test beams were designed to fail in flexure based on conventional reinforced concrete theory without the consideration of potential impact loads, as is usually done. One of the specimens was identified as the reference beam and was tested only under static flexural loading, whereas impact tests were performed on the other four identical large-scale beams. These beams were subjected to different intensities of impact through drop-weight tests. The impact-damaged beams were then subjected to static flexural tests to determine their residual capacities. The dynamic impact and static flexural test results were analyzed. The results were also evaluated in terms of the dynamic force balance, maximum rotational capacity, usability and reparability of the impact-damaged beams. When the analysis results were examined, it was observed that the flexure-dominated behavior of RC beams turns into combined flexural–shear behavior under impact loading, and RC members can reach failure with shear critical behavior. In addition, it was possible to predict the maximum impact energy to be applied to a normal strength beam when we know its static load-carrying and displacement capacity. PubDate: 2023-03-11
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Abstract: Abstract In this paper flow features and heat transfer characteristics of finned and finless double-tube counter flow heat exchanger at wide range of Reynolds numbers were numerically analyzed. Various fins configurations combined with use of water-based TiO2 nanofluid at different nanoparticles volume concentrations were employed in this study to show their effects on nanofluid Nusselt number, friction factor and thermal performance index. Furthermore, the thermal perfection and the overall assessment of heat exchanger were also taken into account in the light of thermodynamics second law efficiency which is defined as a ratio of recovered to expended exergy. The results showed that thermo-hydrodynamical performance of heat exchanger was intensively dependent to the thickness of embedded fins. Employed sensitivity analysis revealed that fins with large thicknesses equal or larger than 10 mm provide better thermal performance than fins with small thicknesses (i.e. t = 1 mm). Furthermore, the use of circular fin with thickness as large as 10 mm at the highest Reynolds number up to about 87,500 led to pronounce both Nusselt number and flow resistance up to 15% and 4.64 folds, respectively. On the other hand, using smooth heat exchanger operating at the lowest Reynolds number (i.e. Re = 3400) filled with 1% TiO2 water-based nanofluid led to obtain the highest recovered exergy and thermodynamic second law efficiency up to 0.46 W and 10.33%, respectively. PubDate: 2023-03-10
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Abstract: Abstract Many agile projects use expert judgment-based methods for estimating effort. Commonly, the judgments made during estimating project features are consensual. However, this will hardly be achieved when a conflict arises between estimators. Besides, estimate depends on the experience and skills of the estimator and could be threatened by his uncertainty to make reliable and accurate assessments. To fill these gaps, an intuitionistic fuzzy expert judgment method is proposed. The latter allows making fuzzified assessments and integrates estimators’ priorities according to a set of human factors. As well, it provides consensual estimates either by the end of the estimation rounds or automatically using an iterative algorithm. On the other hand, an initial empirical study has been conducted on an agile project in which user stories have been estimated by students and experts. The first findings have revealed that the proposal is more suitable for inexperienced estimators or in the first sprints of the project where disagreement is still significant. Nevertheless, when group agreement is increased during the estimating process, the proposal maintains a null bias toward the overestimation or the underestimation of the user stories. PubDate: 2023-03-10
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Abstract: Abstract In this paper, an electric servo actuator implementation scheme with an energy recovery link is proposed to address the potential energy being wasted when a load falls. The new electric servo actuator recovers the potential energy dissipated during the load-falling progress by using an accumulator. The accumulator can also provide extra thrust when the load is lifted. Moreover, a dynamic model is established based on the theory of power bond graphs to analyze the dynamic and energy consumption characteristics of the new electric servo actuator. The simulation results show that the new electric servo actuator can significantly reduce the required motor power and effectively recover the potential energy of the load. This achievement is very significant for improving the energy efficiency of electric servo actuators and mitigating global warming. In addition, the modeling method used in this paper has important reference significance for the energy consumption analysis of other mechanical systems with complex energy domains. PubDate: 2023-03-10
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Abstract: Abstract The drying shrinkage deformation is one of the main factors causing the cracking of ultra-high performance concrete (UHPC). An effective prediction model of drying shrinkage deformation is helpful to prevent the cracking of UHPC by taking precaution measures in construction and improve the service life of UHPC. The drying shrinkage deformation model was established based on the drying shrinkage test of the UHPC with different concrete ingredients (water binder ratio, steel fiber, superplasticizer, silica fume and fly ash). The statistical product and service solutions correlation method was used to analyze the correlation degree of ingredient with drying shrinkage deformation of different UHPCs. The scanning electron microscope and mercury intrusion porosimetry tests were conducted to explain the ingredient-deformation correlation from a microscopic view. Results indicate that the drying shrinkage deformation of UHPC fits well with the composite exponential function model. The drying shrinkage deformation of UHPC can be restrained with the decrease in water binder ratio and at a proper content of steel fiber (25–75 kg/m3) and fly ash (60–100 kg/m3), which can be ascribed to the reduced average pore size and cumulative pore volume of UHPC. Among different concrete ingredients, the water binder ratio is the most significant factor (correlation coefficient with 0.977) influencing the drying shrinkage deformation of UHPC. The findings are of great significance to prepare the UHPC with excellent drying shrinkage resistance. PubDate: 2023-03-10
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Abstract: Abstract This study developed a prediction model of the carbonation depth of concrete containing mineral admixtures based on an intelligent algorithm. A carbonation test database of mineral admixture concrete was established considering the influence of 17 parameters. The intelligent algorithm and three existing carbonation depth prediction models were analysed based on the database. The evaluation results indicated that the prediction accuracy of the back-propagation neural network is higher than that of the support vector machine, and the prediction accuracies of the two intelligent algorithms are higher than those of the existing numerical prediction models for carbonation depth. A variable importance analysis indicated that the content of fly ash in mineral admixture has a relatively large influence on the carbonation depth, and the carbonation time is the most critical factor affecting the carbonation depth of concrete containing mineral admixture. PubDate: 2023-03-09
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Abstract: Abstract In this study, Oregano (Origanum vulgare) leaf essential oil was studied as an environmental-friendly anticorrosion agent for carbon steel in aggressive hydrochloric acid. The corrosion inhibition of O. vulgare was characterized by surface morphology, electrochemical, weight loss, theoretical and computational methods. It was found that the highest inhibition performance of O. vulgare was 85.64% at 2 g/l in 1 M HCl. The results of Langmuir isotherm and adsorption thermodynamics investigation demonstrated that the O. vulgare inhibitor adsorbed on the metal surface by the formation of rigid covalent bonds. The adsorption and inhibition centers of the selected inhibitor were studied by the computational methods, resulting in that the hydroxyl functional groups and benzoyl rings are mainly responsible for the high inhibition efficiency. PubDate: 2023-03-09
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Abstract: Abstract Feature selection is an essential task in the field of machine learning, data mining, and pattern recognition, primarily, when we deal with a large number of features. Feature selection assists in enhancing prediction accuracy, reducing computation time, and creating more comprehensible models. In feature selection, each feature has two possibilities, either it would be taken for computation or not, which implies for n number of features, there are \(2^{n}\) possible feature subsets. So, identifying a relevant feature subset in a reasonable amount of time is an NP-hard problem, but by using an approximation algorithm, a near-optimal solution can be achieved. However, many of the feature selection algorithms use a sequential search strategy to select relevant features, which adds or removes features from the dataset sequentially and leads to trapped into a local optimum solution. In this paper, we propose a novel clustering-based hybrid feature selection approach using ant colony optimization that selects features randomly and measures the qualities of features by K-means clustering in terms of silhouette index and Laplacian score. The proposed feature selection approach allows random selection of features, which allows a better exploration of feature space and thus avoids the problem of being trapped in a local optimal solution, and generates a global optimal solution. The same is verified when compared with another state-of-the-art method. PubDate: 2023-03-09
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Abstract: Abstract Natural rubber (NR) in the form of “cup lump” is used to significantly enhance the thermal stability and elasticity of bitumen. Despite these benefits, the paving industry has raised concerns about its increased energy use and carbon emissions when applied in hot mix asphalt (HMA). Warm mix asphalt (WMA) was invented to reduce this negative effect. In WMA, an additive is first added to the bitumen, which acts as a surfactant and allows the production and compaction of asphalt mixture at temperatures up to 50 °C less. As a result, the amount of energy consumed and carbon dioxide emissions during asphalt production are considerably reduced. In this study, CLR-modified bitumen (CMB) was blended with five percentages of Evotherm warm mix additive (0.3, 0.4, 0.5, 0.6, and 0.75%), and the properties were examined. According to the findings, the Evotherm modification lowered the viscosity of the binder by 26% and the contact angle by 6°, while the binder’s crack resistance at low temperatures marginally improves. Quantitative analysis from Fourier transform infrared spectra revealed a reduction in C=C stretch, C−O stretch, and C−H absorbance in response to the addition of Evotherm. Also, atomic force microscopy (AFM) scan shows an increase in the number of catana phases with a separation of peri- and para-phases. Only CMB with 0.75% Evotherm possesses 100% aggregate coating with sufficient air void; hence, it was selected as the optimum to be used in producing the warm mix asphalt mixture. Overall, the mixing temperature of the CMB can be lowered by 40 °C. PubDate: 2023-03-08
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