Geomechanics and Engineering

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Slope stability prediction using ANFIS models optimized with metaheuristic science

  • Gu, Yu-tian (College of Geoscience & Surveying Engineering, China University of Mining and Technology (Beijing)) ;
  • Xu, Yong-xuan (China Construction Second Engineering Bureau LTD.) ;
  • Moayedi, Hossein (Institute of Research and Development, Duy Tan University) ;
  • Zhao, Jian-wei (School of Electrical and Information Engineering, China University of Mining & Technology (Beijing)) ;
  • Le, Binh Nguyen (Institute of Research and Development, Duy Tan University)
  • Received : 2021.07.20
  • Accepted : 2022.10.28
  • Published : 2022.11.25
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Abstract

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (FS) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the FS behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

Keywords

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