• Geomechanics and Engineering
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• 제27권6호
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• pp.605-614
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• 2021

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.