Journal
JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING
Volume 14, Issue 4, Pages 1188-1199Publisher
SCIENCE PRESS
DOI: 10.1016/j.jrmge.2021.09.010
Keywords
Slope failure time (SFT); Bayesian machine learning (BML); Inverse velocity method (INVM)
Categories
Funding
- Shuguang Program from Shanghai Education Development Foundation
- Shanghai Municipal Education Commission, China [19SG19]
- National Natural Science Foundation of China [42072302]
- Fundamental Research Funds for the Central Universities, China
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This paper introduces a Bayesian machine learning-based method to evaluate the effect of model and observational uncertainties on slope failure time (SFT) prediction. A comprehensive slope failure database is compiled and the application of the method is illustrated with an example. Verification studies show that the method outperforms the traditional inverse velocity method and the maximum likelihood method. This study provides an effective tool for predicting SFT.
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. (C) 2022 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V.
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