Geomechanics and Engineering

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A TBM tunnel collapse risk prediction model based on AHP and normal cloud model

  • Wang, Peng (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Xue, Yiguo (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Su, Maoxin (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Qiu, Daohong (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Li, Guangkun (Geotechnical and Structural Engineering Research Center, Shandong University)
  • Received : 2020.11.15
  • Accepted : 2022.08.12
  • Published : 2022.09.10
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Abstract

TBM is widely used in the construction of various underground projects in the current world, and has the unique advantages that cannot be compared with traditional excavation methods. However, due to the high cost of TBM, the damage is even greater when geological disasters such as collapse occur during excavation. At present, there is still a shortage of research on various types of risk prediction of TBM tunnel, and accurate and reliable risk prediction model is an important theoretical basis for timely risk avoidance during construction. In this paper, a prediction model is proposed to evaluate the risk level of tunnel collapse by establishing a reasonable risk index system, using analytic hierarchy process to determine the index weight, and using the normal cloud model theory. At the same time, the traditional analytic hierarchy process is improved and optimized to ensure the objectivity of the weight values of the indicators in the prediction process, and the qualitative indicators are quantified so that they can directly participate in the process of risk prediction calculation. Through the practical engineering application, the feasibility and accuracy of the method are verified, and further optimization can be analyzed and discussed.

Keywords

Acknowledgement

Much of the work presented in this paper was supported by the Shandong Provincial Natural Science Foundation (grant number ZR2014EEM028), and the National Natural Science Foundation of China (grant numbers 51422904 and 41772298), and the State Key Development Program for Basic Research of China (grant number 2013CB036002). 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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