Updating the models and uncertainty of mechanical parameters for rock tunnels using Bayesian inference

• 作者: Hongbo Zhao Bingrui Chen Shaojun Li Zhen Li Changxing Zhu
• 来源期刊: 地学前缘（英文版）
• 年，卷（期）: 2021,（5）
• 所属期刊栏目: Research Paper
• 页码范围: 224-236
• 页数: 13页
• 语种: 英文

摘要

Rock mechanical parameters and their uncertainties are critical to rock stability analysis, engineering design, and safe construction in rock mechanics and engineering. The back analysis is widely adopted in rock engineering to determine the mechanical parameters of the surrounding rock mass, but this does not consider the uncertainty. This problem is addressed here by the proposed approach by developing a system of Bayesian inferences for updating mechanical parameters and their statistical properties using monitored field data, then integrating the monitored data, prior knowledge of geotechnical parameters, and a mechanical model of a rock tunnel using Markov chain Monte Carlo (MCMC) simulation. The pro-posed approach is illustrated by a circular tunnel with an analytical solution, which was then applied to an experimental tunnel in Goupitan Hydropower Station, China. The mechanical properties and strength parameters of the surrounding rock mass were modeled as random variables. The displacement was pre-dicted with the aid of the parameters updated by Bayesian inferences and agreed closely with monitored displacements. It indicates that Bayesian inferences combined the monitored data into the tunnel model to update its parameters dynamically. Further study indicated that the performance of Bayesian infer-ences is improved greatly by regularly supplementing field monitoring data. Bayesian inference is a sig-nificant and new approach for determining the mechanical parameters of the surrounding rock mass in a tunnel model and contributes to safe construction in rock engineering.