Bayesian Estimation of Rock Mechanical Parameter and Stability Analysis for a Large Underground Cavern
Quan Jiang, Jian Liu, Hong Zheng, Bin Wang, Guo Zhi-zhong, Tao Chen, Xiantao Xiong
Abstract
The uncertainty rock mechanical parameters (i.e., deformation and strength parameters) is an important factor in the safety estimation and support design of underground engineering. Ignoring this uncertainty could allow potential risks to the structure. To address this challenge, this paper develops and verifies a Bayesian approach for a rock’s mechanical parameters estimation by integrating limited site data and prior knowledge, and the integrated knowledge is then transformed into a large number of equivalent samples of the rock’s parameters. The experimental data of marble from triaxial compression tests are first used to verify this method, and the results show that this method can effectively estimate the distribution of the marble’s deformation and strength parameters under the condition of small samples. Further, the probability distribution of rock mass parameters is obtained with the help of Hoek–Brown criterion. Then, the random field of the rock mass with a large cavern is constructed according to the obtained parameter distribution, the influence of different autocorrelation distances is discussed, and the excavation-induced deformation’s statistical analysis is carried out. Finally, the failure of the surrounding rock is characterized probabilistically, which can be a reference to the reliability design of rock support in underground engineering.