A new integrated intelligent computing paradigm for predicting joints shear strength

Abstract

Joints shear strength is a critical parameter during the design and construction of geotechnical engineering structures. The prevailing models mostly adopt the form of empirical functions, employing mathematical regression techniques to represent experimental data. As an alternative approach, this paper proposes a new integrated intelligent computing paradigm that aims to predict joints shear strength. Five metaheuristic optimization algorithms, including the chameleon swarm algorithm (CSA), slime mold algorithm, transient search optimization algorithm, equilibrium optimizer and social network search algorithm, were employed to enhance the performance of the multilayered perception (MLP) model. Efficiency comparisons were conducted between the proposed CSA-MLP model and twelve classical models, employing statistical indicators such as root mean square error (RMSE), correlation coefficient (R²), mean absolute error (MAE), and variance accounted for (VAF) to evaluate the performance of each model. The sensitivity analysis of parameters that impact joints shear strength was conducted. Finally, the feasibility and limitations of this study were discussed. The results revealed that, in comparison to other models, the CSA-MLP model exhibited the most appropriate performance in terms of R² (0.88), RMSE (0.19), MAE (0.15), and VAF (90.32%) values. The result of sensitivity analysis showed that the normal stress and the joint roughness coefficient were the most critical factors influencing joints shear strength. This paper presented an efficacious attempt toward swift prediction of joints shear strength, thus avoiding the need for costly in-site and laboratory tests.

Keywords

Rock discontinuities / Joints shear strength / Metaheuristic optimization algorithms / Machine learning

CRediT authorship contribution statement

Shijie Xie: Conceptualization, Methodology, Validation. Zheyuan Jiang: Resources, Supervision, Visualization, Writing – original draft, Conceptualization. Hang Lin: Funding acquisition, Supervision. Tianxing Ma: Methodology, Software, Writing – review & editing. Kang Peng: Funding acquisition, Software. Hongwei Liu: Formal analysis, Investigation. Baohua Liu: Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This paper gets its funding from Projects (42277175) supported by National Natural Science Foundation of China, Project (2023JJ30657) supported by Hunan Provincial Natural Science Foundation of China and the National Key Research, Hunan Provincial Department of natural resources geological exploration project (BSDZSB43202403), The First National Natural Disaster Comprehensive Risk Survey in Hunan Province (2022-70) and the National Key Research and Development Program of China – 2023 Key Special Project (No. 2023YFC2907400).

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