Optimized prediction models for faulting failure of Jointed Plain concrete pavement using the metaheuristic optimization algorithms

•MOEA/D method has the best performance to select 17 features affecting faulting.•ANN- SAA with R2 value of 0.976 has been the best model for predicting faulting.•Pavement age, cumulative average precipitation, and elasticity modulus of concrete slab are the most important variables. This study aims...

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Vydáno v:Construction & building materials Ročník 364; s. 129948
Hlavní autoři: Ehsani, Mehrdad, Hamidian, Pouria, Hajikarimi, Pouria, Moghadas Nejad, Fereidoon
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 18.01.2023
Témata:
Artificial Neural Networks (ANN)
Faulting Failure
Feature Selection
Jointed Plain Concrete Pavement
Multi-objective Metaheuristic Optimization Algorithms
Feature Selection
Artificial Neural Networks (ANN)
Faulting Failure
Multi-objective Metaheuristic Optimization Algorithms
Jointed Plain Concrete Pavement
ISSN:0950-0618, 1879-0526
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Shrnutí:•MOEA/D method has the best performance to select 17 features affecting faulting.•ANN- SAA with R2 value of 0.976 has been the best model for predicting faulting.•Pavement age, cumulative average precipitation, and elasticity modulus of concrete slab are the most important variables. 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 selection 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 metaheuristic optimization algorithms 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.
ISSN:0950-0618
1879-0526
DOI:10.1016/j.conbuildmat.2022.129948