Abstract
Stability assessment of road cut slopes is not only difficult, but also time-consuming due to the complexity in material properties, coupled with the presence of structural discontinuities. Conventional methods of assessment and simulation of rock slope failures come up with several challenges for geotechnical engineers. This study attempts to develop machine learning models using linear regression (LR), random forest (RF), and support vector regression (SVR) for predicting the vulnerability of jointed rock slopes against planar failure. The dataset used for this study includes unit weight, and cohesion of the rock material, and joint wall compressive strength (JCS), joint roughness coefficient (JRC), and residual friction angle of the joints as input parameters, and the critical strength reduction factor (SRFc) as the output parameter, obtained by simulating 300 jointed rock slope models using the finite element method. Metrics like R2, RMSE, MAE, and MAPE have been used to compare the efficiency of the models. All the three models were highly efficient in predicting the stability, although SVR proved to be slightly better than the other two methods. The correlation among the various features in the dataset revealed that JRC has the maximum influence on the stability of jointed rock slopes. This has been further verified using simple regression, where the predicted output from all the three regression models have been shown to have substantial correlation with the joint roughness coefficient.
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Acknowledgements
The authors acknowledge the Ministry of Education, Government of India, for the Prime Minister’s Research Fellowship and Grant for providing the necessary funding for carrying out this research. The authors are thankful to the Engineering Geology Laboratory of the Indian Institute of Technology (Indian School of Mines) Dhanbad for granting access to the RS2 program for performing the requisite analysis.
Funding
This research was supported by the Prime Minister’s Research Fellowship (PMRF) and Grant (PMRF ID: 1602139).
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All authors have contributed to the conception and design of the study. The conceptualization, data preparation, interpretation, original draft writing, and final editing were done by Avishek Dutta. Kripamoy Sarkar was responsible for supervision, software, review of the first draft of the manuscript, and editing. Analysis and interpretation were done by Keshav Tarun. All the authors have read and approved the final manuscript.
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Dutta, A., Sarkar, K. & Tarun, K. Machine learning regression algorithms for predicting the susceptibility of jointed rock slopes to planar failure. Earth Sci Inform 17, 2477–2493 (2024). https://doi.org/10.1007/s12145-024-01296-5
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DOI: https://doi.org/10.1007/s12145-024-01296-5