Uncertainty Estimation of Rheological Parameters of Soft Rock Using Bayesian Inference and its Application
摘要
Uncertainty in rock rheological parameters is a pervasive issue in soft rock engineering and poses significant challenges for both laboratory-scale parameter calibration and engineering-scale application. This study proposes a Bayesian uncertainty estimation framework for rock rheological parameters based on the stable stage of laboratory step-loading creep tests and Markov Chain Monte Carlo (MCMC) sampling. The rheological parameters of the Burgers model were inferred using creep test data of calcareous mudstone from the Liujiacun section of the Dianzhong Water Diversion Project. The performance of the proposed framework was evaluated in terms of convergence behavior, posterior distributions, parameter correlations, and sensitivity characteristics, demonstrating its ability in quantifying parameter uncertainty. The inferred parameter distributions were then incorporated into finite element simulations to predict long-term tunnel deformation, including crown settlement and horizontal convergence. The predicted deformation envelopes show good agreement with field monitoring data within the 95% confidence interval. Notably, the proposed framework is developed based on the stable creep stage prior to failure and is intended for deformation prediction rather than failure-time forecasting. The findings indicate that the proposed framework enables probabilistic estimation of rheological parameters and their application to engineering-scale deformation prediction, providing a practical tool for understanding the time-dependent behavior of soft rock tunnels.
Highlights Bayesian MCMC framework quantifi es uncertainty in soft rock rheological parameters. Posterior parameters enable probabilistic prediction of long-term tunnel deformation. Predicted deformation aligns with fi eld data within 95% confi dence intervals. Laboratory-to-fi eld parameter transfer achieved through probabilistic simulation.