Geomechanics and Engineering

  • 제22권4호
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  • Pages.291-303
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  • 2020
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Probabilistic analysis of tunnel collapse: Bayesian method for detecting change points

  • Zhou, Binghua (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Xue, Yiguo (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Li, Shucai (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Qiu, Daohong (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Tao, Yufan (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Zhang, Kai (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Zhang, Xueliang (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Xia, Teng (Geotechnical and Structural Engineering Research Center, Shandong University)
  • 투고 : 2019.01.06
  • 심사 : 2020.07.09
  • 발행 : 2020.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

The deformation of the rock surrounding a tunnel manifests due to the stress redistribution within the surrounding rock. By observing the deformation of the surrounding rock, we can not only determine the stability of the surrounding rock and supporting structure but also predict the future state of the surrounding rock. In this paper, we used grey system theory to analyse the factors that affect the deformation of the rock surrounding a tunnel. The results show that the 5 main influencing factors are longitudinal wave velocity, tunnel burial depth, groundwater development, surrounding rock support type and construction management level. Furthermore, we used seismic prospecting data, preliminary survey data and excavated section monitoring data to establish a neural network learning model to predict the total amount of deformation of the surrounding rock during tunnel collapse. Subsequently, the probability of a change in deformation in each predicted section was obtained by using a Bayesian method for detecting change points. Finally, through an analysis of the distribution of the change probability and a comparison with the actual situation, we deduced the survey mark at which collapse would most likely occur. Surface collapse suddenly occurred when the tunnel was excavated to this predicted distance. This work further proved that the Bayesian method can accurately detect change points for risk evaluation, enhancing the accuracy of tunnel collapse forecasting. This research provides a reference and a guide for future research on the probability analysis of tunnel collapse.

키워드

과제정보

Much of the work presented in this paper was supported by the National Natural Science Foundations of China (grant numbers 41877239, 51379112, 51422904 and 41772298), and the State Key Development Program for Basic Research of China (grant number 2013CB036002), and the Fundamental Research Funds of Shandong University (grant number 2018JC044), and Shandong Provincial Natural Science Foundation (grant number JQ201513). The authors would like to express appreciation to the reviewers for their valuable comments and suggestions that helped improve the quality of our paper.

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