Reliability Analysis of Unsaturated Soil Slope Stability Using Spatial Random Fields-Based Bayesian Method

Rainfall and water level change are two main factors causing failure of reservoir slopes. Thus soil-fluid mechanics is applied with the fluctuation of groundwater table. However, many geotechnical parameters needed for the analysis are highly varied. Spatial random fields-based Bayesian method is proposed, which can systematically assimilate prior knowledge, borehole test data and long-term monitoring data to obtain posterior distributions. There are three major components of the method. They include unsaturated soil-fluid coupling mechanics, spatial multivariate discretization, and subset Monte-Carlo simulation of reliability analysis. The approach is applied to the Shiliushubao slope of the Three Gorges Reservoir area, which is located 9.0km downstream of the Badong County in Hubei Province, China. Results prove that the updated key geotechnical parameters will quantitatively predict the geohazard of landslide, especially for unsaturated soil slope that suffers an unprecedented heavy rainfall subject to low reservoir water level in the summer. According to the conditions of heavy rainfall and rapid rise and fall of the Yangtze River water level in 2020, the reliability analysis of the old Shiliushubao landslide is carried out in the Three Gorges Reservoir area. The results for the two different conditions both indicate that the safety factor of the slope stability is low, but the reliability index meets the minimum requirement of 2.70 according to the specification. None of the additional reinforcement measures are required.

Keywords: Geotechnical parameters, spatial random fields, Bayesian method, Unsaturated soil slope, Reliability analysis