Enhancing Bayesian probabilistic back-analysis efficiency using multi-type surface and subsurface monitoring data: Case study of the Baihetan left bank slope

Abstract

In large-scale hydropower project construction, comprehensive internal and external deformation monitoring (periodic observation) of high-steep rock slopes is crucial for revealing slope deformation and rock mass deterioration. However, few studies have examined the impact of using both surface and subsurface monitoring data on the performance of probabilistic back-analysis (PBA). This study aims to fill this gap by developing an improved Bayesian PBA method. Using the left bank excavation slope of the Baihetan (BHT) hydropower station in China as a case study, we probabilistically calibrated the geotechnical parameters of the slope using multi-type surface (robotic total stations) and subsurface (multi-point displacement meters and inclinometers) monitoring data. The results indicate that compared to using a single type of monitoring data, using multiple types of monitoring data can further reduce the uncertainty of geotechnical parameters. Specifically, integrating surface and subsurface monitoring data for back-analysis can achieve an optimal three-dimensional model prediction and yield a reasonable parameter set. When there are differences in monitoring data stability, incorporating relatively stable monitoring (minor deformation) data into the Bayesian back-analysis can help improve the convergence speed of Bayesian sequential inversion. However, appropriate methods are required to evaluate the contribution of these data to the back-analysis model.