Probabilistic back analysis for geotechnical engineering based on Bayesian and support vector machine

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.

Keywords

geotechnical engineering / back analysis / uncertainty / Bayesian theory / least square method / support vector machine (SVM)

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