Proceedings of the
8th International Symposium on Geotechnical Safety and Risk (ISGSR)
14 – 16 December 2022, Newcastle, Australia
Editors: Jinsong Huang, D.V. Griffiths, Shui-Hua Jiang, Anna Giacomini, Richard Kelly
doi:10.3850/978-981-18-5182-7_07-002-cd
Bayesian Updating of Slope Reliability under Rainfall Infiltration with Field Observations
1School of Infrastructure Engineering, Nanchang University, Nanchang, Jiangxi 330031, China.
2School of Civil Engineering, Sun Yat-Sen University, Zhuhai 519082, China.
3Engineering Risk Analysis Group, Technische Universität München. Arcisstraße 21, 80333 Munich, German
ABSTRACT
Reliability analyses of rainfall-induced slope stability often ignore the effect of field observations, including the observation that the slope remains stable under its natural condition and/or that the slope survives under a past extreme rainfall event. In this paper, the BUS (Bayesian Updating with Structural reliability methods) method, originally proposed by Straub and Papaioannou (2015), is employed to conduct Bayesian inverse analyses of spatially varying hydraulic and shear strength parameters with the field observations. Stationary lognormal random field models are established to depict the spatial distribution features of the hydraulic and shear strength parameters. An infinite slope model is taken as an example to evaluate the probability of slope failure under a target rainfall event within the framework of Monte-Carlo simulation. This analysis allows incorporating the field observations to evaluate the rainfall-induced slope failure probability in spatially variable soils. The research outcomes can provide a new perspective to understand the rainfall-induced slope failure mechanism.
Keywords: slope reliability, rainfall infiltration, spatial variability, Bayesian inverse analysis, field observation.
