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http://dx.doi.org/10.12989/sss.2020.26.3.263

Metaheuristic-hybridized multilayer perceptron in slope stability analysis  

Ye, Xinyu (School of civil engineering, Central South University)
Moayedi, Hossein (Institute of Research and Development, Duy Tan University)
Khari, Mahdy (Department of Civil Engineering, East Tehran Branch, Islamic Azad University)
Foong, Loke Kok (Department for Management of Science and Technology Development, Ton Duc Thang University)
Publication Information
Smart Structures and Systems / v.26, no.3, 2020 , pp. 263-275 More about this Journal
Abstract
This research is dedicated to slope stability analysis using novel intelligent models. By coupling a neural network with spotted hyena optimizer (SHO), salp swarm algorithm (SSA), shuffled frog leaping algorithm (SFLA), and league champion optimization algorithm (LCA) metaheuristic algorithms, four predictive ensembles are built for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The data used to develop the ensembles are provided from a vast finite element analysis. After creating the proposed models, it was observed that the best population size for the SHO, SSA, SFLA, and LCA is 300, 400, 400, and 200, respectively. Evaluation of the results showed that the combination of metaheuristic and neural approaches offers capable tools for estimating the FOS. However, the SSA (error = 0.3532 and correlation = 0.9937), emerged as the most reliable optimizer, followed by LCA (error = 0.5430 and correlation = 0.9843), SFLA (error = 0.8176 and correlation = 0.9645), and SHO (error = 2.0887 and correlation = 0.8614). Due to the high accuracy of the SSA in properly adjusting the computational parameters of the neural network, the corresponding FOS predictive formula is presented to be used as a fast yet accurate substitution for traditional methods.
Keywords
geotechnical engineering; slope stability analysis; neural computing; metaheuristic optimizer;
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Times Cited By KSCI : 9  (Citation Analysis)
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