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Sahoo, Arun, Tripathy, Debi, Jayanthu, Singam. (1403). Application of Machine Learning Techniques in Slope Stability Analysis: A Comprehensive Overview. , 15(3), 907-921. doi: 10.22044/jme.2024.13881.2583
Arun Kumar Sahoo; Debi Prasad Tripathy; Singam Jayanthu. "Application of Machine Learning Techniques in Slope Stability Analysis: A Comprehensive Overview". , 15, 3, 1403, 907-921. doi: 10.22044/jme.2024.13881.2583
Sahoo, Arun, Tripathy, Debi, Jayanthu, Singam. (1403). 'Application of Machine Learning Techniques in Slope Stability Analysis: A Comprehensive Overview', , 15(3), pp. 907-921. doi: 10.22044/jme.2024.13881.2583
Sahoo, Arun, Tripathy, Debi, Jayanthu, Singam. Application of Machine Learning Techniques in Slope Stability Analysis: A Comprehensive Overview. , 1403; 15(3): 907-921. doi: 10.22044/jme.2024.13881.2583

Application of Machine Learning Techniques in Slope Stability Analysis: A Comprehensive Overview

Journal of Mining and Environment
مقاله 6، دوره 15، شماره 3، مرداد 2024، صفحه 907-921    اصل مقاله (2.34 M)
نوع مقاله: Review Paper
شناسه دیجیتال (DOI): 10.22044/jme.2024.13881.2583
نویسندگان
Arun Kumar Sahoo* ؛ Debi Prasad Tripathy؛ Singam Jayanthu
Department of Mining Engineering, National Institute of Technology, Rourkela, India
چکیده
The mining industry needs to accept new-age autonomous technologies and intelligent systems to stay up with the modernization of technology, to benefit the shake of investors and stakeholders, and most significantly, for the nation, and to protect health and safety. An essential part of geo-technical engineering is doing slope stability analysis to determine the likelihood of slope failure and how to prevent it. A reliable, cost-effective, and generally applicable technique for evaluating slope stability is urgently needed. Numerous research studies have been conducted, each employing a unique strategy. An alternate method that uses machine learning (ML) techniques is to study the relationship between stability conditions and slope characteristics by analyzing the data collected from slope monitoring and testing. This paper is an attempt by the authors to comprehensively review the literature on using the ML techniques in slope stability analysis. It was found that most researchers relied on data-driven approaches with limited input variables, and it was also verified that the ML techniques could be utilized effectively to predict slope failure analysis. SVM and RF were the most popular types of ML models being used. RMSE and AUC were used extensively in assessing the performance of the ML models.
کلیدواژه‌ها
Slope stability؛ Factor of safety؛ Machine Learning models؛ Support vector machine؛ Random Forest
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