Volume 12 Issue 4
Jul.  2021
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Minzong Zheng, Shaojun Li, Hongbo Zhao, Xiang Huang, Shili Qiu. Probabilistic analysis of tunnel displacements based on correlative recognition of rock mass parameters[J]. Geoscience Frontiers, 2021, 12(4): 101136. doi: 10.1016/j.gsf.2020.12.015
Citation: Minzong Zheng, Shaojun Li, Hongbo Zhao, Xiang Huang, Shili Qiu. Probabilistic analysis of tunnel displacements based on correlative recognition of rock mass parameters[J]. Geoscience Frontiers, 2021, 12(4): 101136. doi: 10.1016/j.gsf.2020.12.015

Probabilistic analysis of tunnel displacements based on correlative recognition of rock mass parameters

doi: 10.1016/j.gsf.2020.12.015

This study was financially supported by the National Natural Science Foundation of China (Grant Nos. U1765206, 51621006 and 41877256) and Innovation Research Group Project of Natural Science Foundation of Hubei Province (ZRQT2020000114). We are grateful to the Associate Editor Biswajeet Pradhan and anonymous reviewers for their constructive comments. These suggestions helped us with the revision and improvement of this paper.

  • Received Date: 2020-08-09
  • Rev Recd Date: 2020-12-26
  • Displacement is vital in the evaluations of tunnel excavation processes, as well as in determining the post-excavation stability of surrounding rock masses. The prediction of tunnel displacement is a complex problem because of the uncertainties of rock mass properties. Meanwhile, the variation and the correlation relationship of geotechnical material properties have been gradually recognized by researchers in recent years. In this paper, a novel probabilistic method is proposed to estimate the uncertainties of rock mass properties and tunnel displacement, which integrated multivariate distribution function and a relevance vector machine (RVM). The multivariate distribution function is used to establish the probability model of related random variables. RVM is coupled with the numerical simulation methods to construct the nonlinear relationship between tunnel displacements and rock mass parameters, which avoided a large number of numerical simulations. Also, the residual rock mass parameters are taken into account to reflect the brittleness of deeply buried rock mass. Then, based on the proposed method, the uncertainty of displacement in a deep tunnel of CJPL-II laboratory are analyzed and compared with the in-situ measurements. It is found that the predicted tunnel displacements by the RVM model closely match with the measured ones. The correlations of parameters have significant impacts on the uncertainty results. The uncertainty of tunnel displacement decreases while the reliability of the tunnel increases with the increases of the negative correlations among rock mass parameters. When compared to the deterministic method, the proposed approach is more rational and scientific, and also conformed to rock engineering practices.

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