This study aims to predict faulting failure of jointed plain concrete pavement (JPCP) using different variables. For this purpose, four feature selection methods were developed by combining the artificial neural netwo...
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This study aims to predict faulting failure of jointed plain concrete pavement (JPCP) using different variables. For this purpose, four feature selection methods were developed by combining the artificial neural networks (ANN) and four multi-objective metaheuristic optimization algorithms, namely, the Pareto envelope-based se-lection algorithm II (PESA-2), the strength Pareto evolutionary algorithm 2 (SPEA-2), multi-objective particle swarm optimization (MPSO), and multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D showed better performance compared to the other models, which identified 17 input variables affecting faulting failure. In the next step, the classic back-propagation (BP), Biogeography-based optimization (BBO), invasive weed optimization (IWO), and simulated annealing algorithm (SAA) were combined with the ANN to develop three prediction models for faulting failure. Modeling with metaheuristicoptimization algo-rithms showed better performance than the ordinary ANN. The pavement age, cumulative average precipitation, and elasticity modulus of the concrete slab have the most significant impact on the formation and increase of faulting.
Pansharpening aims to fuse a lower-resolution multispectral (MS) image and a higher-resolution panchromatic image, resulting in an image with the color quality of the former and spatial detail quality of the latter. O...
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Pansharpening aims to fuse a lower-resolution multispectral (MS) image and a higher-resolution panchromatic image, resulting in an image with the color quality of the former and spatial detail quality of the latter. Of all, the component substitution (CS)-based pansharpening methods have drawn attentions with their ability to produce sharp images. Despite their success in sharpening the images, these methods deteriorate the color features of the input MS images due of the uncertainty in the calculation of the intensity component used by them. Previous studies showed that attempts to preserve the color features tend to cause spatial detail loss to a certain extent. This, of course, reveals the necessity of a compromise between the spectral and spatial fidelity of the pansharpened images produced by the CS-based techniques. This study proposed using the multi-objective Non-Dominated Sorting Genetic Algorithm-II metaheuristic algorithm with the CS-based methods to optimize the intensity component to find the best compromise between the spectral and spatial fidelity of the pansharpened images. The proposed framework was applied on two commonly used pansharpening techniques, Gram-Schmidt and Synthetic Variable Ratio. It was found that the proposed methods managed to find the best balance between the color and spatial fidelity.
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