Geomechanics and Engineering

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Using Bayesian network and Intuitionistic fuzzy Analytic Hierarchy Process to assess the risk of water inrush from fault in subsea tunnel

  • Song, Qian (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Xue, Yiguo (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Li, Guangkun (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) ;
  • Kong, Fanmeng (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Zhou, Binghua (Geotechnical and Structural Engineering Research Center, Shandong University)
  • Received : 2021.01.19
  • Accepted : 2021.11.22
  • Published : 2021.12.25
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Abstract

Water inrush from fault is one of the most severe hazards during tunnel excavation. However, the traditional evaluation methods are deficient in both quantitative evaluation and uncertainty handling. In this paper, a comprehensive methodology method combined intuitionistic fuzzy AHP with a Bayesian network for the risk assessment of water inrush from fault in the subsea tunnel was proposed. Through the intuitionistic fuzzy analytic hierarchy process to replace the traditional expert scoring method to determine the prior probability of the node in the Bayesian network. After the field data is normalized, it is classified according to the data range. Then, using obtained results into the Bayesian network, conduct a risk assessment with field data which have processed of water inrush disaster on the tunnel. Simultaneously, a sensitivity analysis technique was utilized to investigate each factor's contribution rate to determine the most critical factor affecting tunnel water inrush risk. Taking Qingdao Kiaochow Bay Tunnel as an example, by predictive analysis of fifteen fault zones, thirteen of them are consistent with the actual situation which shows that the IFAHP-Bayesian Network method is feasible and applicable. Through sensitivity analysis, it is shown that the Fissure development and Apparent resistivity are more critical comparing than other factor especially the Permeability coefficient and Fault dip. The method can provide planners and engineers with adequate decision-making support, which is vital to prevent and control tunnel water inrush.

Keywords

Acknowledgement

The authors would like to thank the financial support from the National Natural Science Foundation of China (grant numbers 41877239, 51379112, 51422904, 40902084 and 41772298), and Fundamental Research Fund of Shandong University (grant number 2018JC044), and Natural Science Foundation of Shandong Province (grant numbers JQ201513 and 2019GSF111028).

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