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Prediction of risk in submarine tunnel construction by multi-factor analysis: A collapse prediction model

Abstract Collapse is a major threat in tunnel construction. How to predict collapse risk accurately and timely is a complicated problem. In this paper, a non-linear mathematical model is applied to obtain the potential risk indices for a submarine tunnel using statistical analysis based on previous submarine tunnels, such as the Seikan tunnel and Xiang’an tunnel. Rough set theory is used to screen risk indices, determine each indicator value, and classify risk factors. Traditional weight calculation methods that are overly dependent on expert experience and other subjective factors are optimized and improved. Based on the frequency of each risk factor, the objective weight value of each risk index is determined according to weight back analysis theory. The ideal point method is used to calculate the collapse risk level. Predictions made by this new method are consistent with the actual tunnel collapse risk levels. This new method provides theoretical and technical basis for effectively predicting tunnel collapse.