Sequential Bayesian Updating of Spatially Varying Soil Parameters and Probability of Failure Induced by Rainfall Using Slope Performance Records

Predicting the probability of slope failure caused by rainfall is pivotal to landslide risk assessment and mitigation. Soil properties are inherently spatially variable and often exhibit uncertainties due to a lack of site investigation data, which can lead to inaccurate estimation of probability of slope failure. Recent studies showed that slope performance records (i.e., the slope stays stable or fails after a heavy rainfall) can be used as a valuable source of information to reduce the uncertainties of soil parameters and enhance the estimate of probability of slope failure. This study proposes to successively update the distributions of soil parameters and probability of slope failure by integrating three pieces of slope performance records (i.e., slope keeping stable before rainfall, slope keeping stable after 57 days of weak rainfall, and slope instability after continuous 3 days of heavy rainfall). Bayesian Updating with Subset simulation (BUS) approach is adopted to enhance the computational efficiency of Bayesian analysis. The updated results are then used to predict the probability of slope failure under a future rainfall. The results indicate that the sequential updating greatly reduces the uncertainties in soil parameters and contributes to accurately predict the probability of slope failure under a future rainfall.