Prediction of long-term deformation of slope in Baihetan hydropower station based on updated rheological parameters with Bayesian estimation

Large-scale slope engineering projects are typically characterized by prolonged construction periods and extended service lives. The long-term deformation of rock masses can lead to geological disasters, such as mass collapse and sliding. Therefore, accurate prediction of long-term slope deformation is essential for both engineering design and disaster mitigation. This study proposes an integrated prediction framework that combines a rheological constitutive model, Bayesian estimation, and numerical simulation, and applies it to the left-bank slope of the Baihetan Hydropower Station. Triaxial creep compression tests with step loading were conducted, and the experimental data were used as observation inputs. The Burgers model was used as the theoretical foundation, and Bayesian estimation was employed to update the rheological parameters. Subsequent sensitivity and correlation analyses were conducted to evaluate parameter behavior. Finally, the updated parameters were incorporated into a numerical model to simulate the long-term deformation of the slope. The results demonstrate that the Bayesian estimation method effectively reduces and quantifies the uncertainty associated with rheological parameters. Additionally, the simulation results based on the calibrated parameters exhibit good agreement with the field monitoring data. These findings provide valuable insights for quantifying rheological uncertainty and enhancing the prediction of long-term deformation in slope engineering.