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Two novel combined systems for predicting the peak shear strength using RBFNN and meta-heuristic computing paradigms

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Abstract

Effective prediction of the peak shear strength (PSS) is of crucial importance in evaluating the stability of a rock slope with interlayered rocks and has both theoretical and practical significance. This paper offers two novel prediction tools for the PSS prediction based on radial basis function neural network (RBFNN) and meta-heuristic computing paradigms. For this work, the gray wolf optimization (GWO) and ant colony optimization (ACO) algorithms were used to select the optimal parameters of RBFNN. Then, these two new models were compared with the gene expression programming (GEP) model. A total of 158 experimental data were used to train and test the proposed models using three input parameters, i.e., normal stress, compressive strength ratio of joint walls, and joint roughness coefficient. Finally, the computational result revealed that the RBFNN-GWO model, with the coefficient of determination (R2) of 0.997, produced a better convergence speed and higher accuracy compared with RBFNN-ACO and GEP models, with the R2 of 0.995 and 0.996, respectively. The RBFNN-GWO model was found an efficient predictive tool that can help rock engineers in the slopes design processes.

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Authors and Affiliations

  1. State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, 116000, China

    Juncheng Gao

  2. China Vanke Co., Ltd., Shenzhen, 518000, China

    Juncheng Gao

  3. Département Études Thermodynamiques, Division Laboratoires, Sonatrach, Boumerdes, Algeria

    Menad Nait Amar

  4. Department of Civil Engineering, Faculty of Engineering, Arak University, Arak, Iran

    Mohammad Reza Motahari

  5. Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam

    Mahdi Hasanipanah

  6. Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Danial Jahed Armaghani

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  1. Juncheng Gao
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  2. Menad Nait Amar
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  3. Mohammad Reza Motahari
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  4. Mahdi Hasanipanah
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  5. Danial Jahed Armaghani
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Correspondence to Mahdi Hasanipanah.

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Gao, J., Nait Amar, M., Motahari, M.R. et al. Two novel combined systems for predicting the peak shear strength using RBFNN and meta-heuristic computing paradigms. Engineering with Computers 38, 129–140 (2022). https://doi.org/10.1007/s00366-020-01059-y

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