JRMGE / Vol 14 / Issue 4
Bayesian machine learning-based method for prediction of slope failure time
Jie Zhang, Zipeng Wang, Jinzheng Hu, Shihao Xiao, Wenyu Shang
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a Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai, 200092, China
b Department of Geotechnical Engineering, Tongji University, Shanghai, 200092, China
c Natural Science College, Michigan State University, MI, 48825, USA
2022, 14(4): 1188-1199. doi:10.1016/j.jrmge.2021.09.010
Received: 2021-05-09 / Revised: 2021-08-14 / Accepted: 2021-09-09 / Available online: 2021-11-20
2022, 14(4): 1188-1199.
doi:10.1016/j.jrmge.2021.09.010
Received: 2021-05-09
Revised: 2021-08-14
Accepted: 2021-09-09
Available online: 2021-11-20
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.
Keywords: Slope failure time (SFT), Bayesian machine learning (BML), Inverse velocity method (INVM)
Article Data
Author(s) Information
Jie Zhang
Dr. Jie Zhang is a professor at the Department of Geotechnical Engineering of Tongji University, China. He received his PhD degree in Civil Engineering from The Hong Kong University of Science and Technology in 2009. He is currently the secretary of the Engineering Practice of Risk Assessment and Management Committee of the International Society of Soil Mechanics and Geotechnical Engineering (TC304, ISSMGE), and one of the founding managing editors of the journal of Underground Space. His research mainly focuses on probabilistic analysis and assessment of geohazards. He is the recipient of several academic awards, including the Outstanding Paper Award from Computers and Geotechnics (2017), the Young Researcher Award from Geotechnical Safety Network (GEOSNet) (2017), and the Natural Science Award from the Ministry of Education of China (2018).