Probabilistic back analysis for geotechnical engineering based on Bayesian and support vector machine
来源期刊：中南大学学报(英文版)2015年第12期
论文作者：CHEN Bing-rui ZHAO Hong-bo RU Zhong-liang LI Xian
文章页码：4778 - 4786
Key words：geotechnical engineering; back analysis; uncertainty; Bayesian theory; least square method; support vector machine (SVM)
Abstract: Geomechanical parameters are complex and uncertain. In order to take this complexity and uncertainty into account, a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine (LS-SVM) technique was proposed. The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters, and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters. The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China. The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well, and also improves the understanding that the monitored information is important in real projects.
CHEN Bing-rui(陈炳瑞)1, ZHAO Hong-bo(赵洪波)2, RU Zhong-liang(茹忠亮)2, LI Xian(李贤)3
(1. State Key Laboratory of Geomechanics and Geotechnical Engineering,
Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China;
2. School of Civil Engineering, Henan Polytechnic University, Jiaozuo 454003, China;
3. School of Resource and Environmental Engineering, Wuhan University of Science and Technology,
Wuhan 430081, China)
Abstract:Geomechanical parameters are complex and uncertain. In order to take this complexity and uncertainty into account, a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine (LS-SVM) technique was proposed. The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters, and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters. The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China. The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well, and also improves the understanding that the monitored information is important in real projects.
Key words:geotechnical engineering; back analysis; uncertainty; Bayesian theory; least square method; support vector machine (SVM)
