Bayesian machine learning-based method for prediction of slope failure time  

作　　者：Jie Zhang Zipeng Wang Jinzheng Hu Shihao Xiao Wenyu Shang 

机构地区：[1]Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education,Tongji University,Shanghai,200092,China [2]Department of Geotechnical Engineering,Tongji University,Shanghai,200092,China [3]Natural Science College,Michigan State University,MI,48825,USA

出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》2022年第4期1188-1199,共12页岩石力学与岩土工程学报（英文版）

基　　金：substantially supported by the Shuguang Program from Shanghai Education Development Foundation;Shanghai Municipal Education Commission, China (Grant No. 19SG19);National Natural Science Foundation of China (Grant No. 42072302);Fundamental Research Funds for the Central Universities, China

摘　　要：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.

关 键 词：Slope failure time(SFT) Bayesian machine learning(BML) Inverse velocity method(INVM) 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TU43[自动化与计算机技术—控制科学与工程]