Quantitative inversion of accidents is an important work of finding the cause of accidents and avoiding their recurrence. However, quantitative inversion of accidents is difficult due to the lack and limitation of accidents monitoring information. Focusing on water-inrush incidents of Jiguan Mountain tunnel, this paper proposes a set of workflows to find out the missing conditions and quantitative inversion of accidents by flow analysis and structural safety analysis on the basis of investigating the rain capacity and water outflow in water-inrush incidents. First, hydraulic boundary in water-inrush incidents is acquired by analyzing the relationship of catchment, infiltration, and accumulation of rainwater in karst pit using the flooding algorithm of ArcGIS and the topographic mapping of UAV photogrammetry. Second, the permeability coefficients of karst infiltration zone and tunnel surrounding rock are acquired by two-step decoupling and inverse analyzing the water inflow, flow rate, and interval time between rainfall and water inrush. Third, tunnel accidents of the overload of tunnel lining induced by the catchment and infiltration of karst pit under extreme rainfall conditions are numerically simulated by using FLAC. The results indicate that quantitative inversion of water-inrush incidents reveals the process and cause of accidents and provides the safety index of tunnel structure. Not only is the water-inrush incidents of karst tunnel controlled by hydrogeology conditions, but also the rainfall recharge should not be ignored.

Tunnel collapse in the karst tunnel occurs suddenly. Dynamic risk assessment for tunnel collapse is more accurate than static analysis, which is not enough at the stage. The study designs a new questionnaire to establish dynamic risk assessment for karst tunnels collapse, by a fuzzy analytic hierarchy process (F-AHP) method. The characteristics of the cave, dynamic monitoring, and prediction are fully considered in the assessment to strengthen the karst and dynamic characters: (1) the factors of dynamic risk assessment are selected based on advanced geological prediction, collapse investigation, and theoretical analysis as dynamic and static factors. Dynamic factors are classified as the rationality of advanced geological prediction method, reliability of data, the accuracy of data analysis, and timeliness and effectiveness of forecast information transmission. Karst cave characteristic factors are composed of cave scales, locations, and thickness of rock plate, based on collapse investigation and theoretical analysis to strengthen the character of karst. (2) A new questionnaire is designed in the consulting process to express the relative importance of factors by combining a Saaty scale method and a designed three-scale method. The judgment matrix by the new questionnaire can satisfy the consistency requirement, which is hard to satisfy in the traditional F-AHP method. (3) The dynamic risk assessment is carried out on different samples in the Lianhuashan tunnel. By comparing the dynamic assessment results with the occurrence of disasters, the rationality of the assessment is verified.