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Bayesian machine learning-based method for prediction of slope failure time
Bayesian machine learning-based method for prediction of slope failure time
Jie Zhang
(Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai, 200092, China; Department of Geotechnical Engineering, Tongji University, Shanghai, 200092, China)
Zipeng Wang
(Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai, 200092, China; Department of Geotechnical Engineering, Tongji University, Shanghai, 200092, China)
Jinzheng Hu
(Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai, 200092, China; Department of Geotechnical Engineering, Tongji University, Shanghai, 200092, China; Corresponding author. Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai, 200092, China.)
Shihao Xiao
(Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai, 200092, China; Department of Geotechnical Engineering, Tongji University, Shanghai, 200092, China)
Wenyu Shang
(Natural Science College, Michigan State University, MI, 48825, USA)
Journal: Journal of Rock Mechanics and Geotechnical Engineering
•
• Elsevier
• ISSN: 1674-7755
Abstract
The data-driven phenomenological models based on deformation measurements have been widely utilized to predict the slope failure time (SFT). The observational and model uncertainties could lead the predicted SFT calculated from the phenomenological models to deviate from the actual SFT. Currently, very limited study has been conducted on how to evaluate the effect of such uncertainties on SFT prediction. In this paper, a comprehensive slope failure database was compiled. A Bayesian machine learning (BML)-based method was developed to learn the model and observational uncertainties involved in SFT prediction, through which the probabilistic distribution of the SFT can be obtained. This method was illustrated in detail with an example. Verification studies show that the BML-based method is superior to the traditional inverse velocity method (INVM) and the maximum likelihood method for predicting SFT. The proposed method in this study provides an effective tool for SFT prediction.
Jie Zhang, Zipeng Wang, Jinzheng Hu, et al. (2022). "Bayesian machine learning-based method for prediction of slope failure time". Journal of Rock Mechanics and Geotechnical Engineering. https://doi.org/10.1016/j.jrmge.2021.09.010