Pseudo-static slope stability analysis using explainable machine learning techniques

Kenue Abdul Waris (), Sheikh Junaid Fayaz (), Alluri Harshith Reddy () and B. Munwar Basha ()
Additional contact information
Kenue Abdul Waris: Indian Institute of Technology Hyderabad
Sheikh Junaid Fayaz: Indian Institute of Technology Delhi
Alluri Harshith Reddy: Indian Institute of Technology Hyderabad
B. Munwar Basha: Indian Institute of Technology Hyderabad

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 1, No 20, 485-517

Abstract: Abstract This research focuses on developing the optimal machine learning (ML) based predictive model for calculating the factor of safety (FSMP) for finite slopes using the Morgenstern-Price method of slices. The ML models utilize geometric and geotechnical parameters, including slope angle, friction angle, cohesion, slope height, unit weight, horizontal seismic acceleration coefficient, and the ratio of horizontal to vertical seismic acceleration coefficients. A comprehensive dataset of 19,128 data points is generated using in-house MATLAB code. These data points are trained with the ML models to learn the underlying correlations for the prediction of FSMP. Various ML predictive models, such as multiple linear regression, support vector regression, Gaussian process regression, random forest, extreme gradient boosting, and artificial neural networks, are considered for constructing the optimal model. The objective is to develop a tailored framework for arriving at the best-performing predictive model for replication of pseudo-static stability analysis of soil slopes in geotechnical engineering. Comparison of different data-driven models are also presented. The study also utilized interpretable machine learning models with Shapley values to mitigate the inherent “black box” nature of ML models. The study also establishes a physically interpretable error validation model to assess model predictions. The findings illustrate the effectiveness and precision of the Gaussian process regression (GPR) model, as evidenced by R2 error metric values of 99.9% and 99.8% for the training and test sets, respectively. Further, the error metric for the artificial neural network (ANN) achieved values of 99.7% and 99.6% for the training and test sets, respectively. The GPR model offers conservative results over ANN, making it the preferred predictive model for safe FSMP predictions. It serves as an efficient estimation tool for field practitioners, can be integrated into smartphones and above all integrated into the performance function for uncertainty quantification in the otherwise computationally expensive Monte Carlo simulations. Design charts are also generated using the selected optimal model for depicting the generalizability of this model, enabling geotechnical engineers to determine FSMP without complex calculations. This research showcases the potential of ML techniques for complex geotechnical problems, advancing conventional slope stability analysis and opening avenues for their practical and reliable use in geotechnical engineering.

Keywords: Predictive modelling; Slope stability analysis; Interpretable machine learning; Pseudo-static; Limit equilibrium method; SHAP analysis; Design charts (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-024-06839-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:121:y:2025:i:1:d:10.1007_s11069-024-06839-z

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-024-06839-z

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().