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

• DOI: 10.1016/j.gsf.2021.101198
• Publication Date: 2021-04-05T23:07:22Z
• Authors: Hongbo Zhao; Bingrui Chen; Shaojun Li; Zhen Li; Changxing Zhu
• Keywords: Lagrangian analysis

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