In order to evaluate the risk level of water inrush caused by karst cave accurately and effectively, a novel quantitative assessment model was established based on the reliability theory and genetic algorithm-back propagation (GA-BP) neural network. First, the reliability theory and the calculation formula of the minimum safe thickness were used to calculate the water inrush probability. Second, the GA-BP neural network was applied to predict the disaster consequence caused by water inrush. Six factors, including water pressure, hydraulic supply, type of gap, filling situation, degree of water enrichment and reserves of cave, were selected as the input layer of the neural network. The disaster consequence was selected as the output layer. Similar projects were screened to obtain statistical information for indices, and the Normand function in MATLAB was used to transform the information into quantitative data. Finally, the model was established by combining the probability and disaster consequence of water inrush. The 602cave in Yesanguan tunnel was taken as an engineering sample to verify the feasibility of the novel model. The obtained results showed that the proposed model is comprehensive and accurate in quantitative assessment, which has good application prospects in engineering.

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Tunnel excavation has always been an important reason for the stability failure of the tunnel-slope system at the portal section. In case of rainfall, it is likely to cause serious disasters such as tunnel vault collapse, water inrush from the tunnel face, and slope slip. In this study, the Sunjiaya tunnel of the Daping slope group was taken as the engineering background, and the tunnel model experiment system under the condition of rainfall and groundwater seepage was designed independently to explore the failure laws of slope instability induced by tunnel excavation under the condition of rainfall. Meanwhile, a fiber grating monitoring system was also used to measure the displacement, water content, earth pressure, and seepage pressure at different positions of the tunnel-slope system in the process of tunnel excavation under the condition of rainfall. The results show that the slope instability caused by rainfall infiltration is gradual. At the beginning of rainfall, the rainfall infiltration has little effect on the stability of the tunnel-slope system and then gradually increases with the continuous rainfall. Finally, the slope surface is uneven, and the phenomenon of gully and surface flow is serious. The foot of slope moves back continuously, resulting in overall collapse. Moreover, during the process of tunnel excavation, the cracks on the tunnel vault of the unburied tunnel lining develop in quadratic along the tunnel excavation direction, and the closer to the excavation section, the larger the collapse range. Finally, there is an integral collapse of the vault of tunnel excavation section. In addition, variation laws of parameters in the tunnel-slope system also provide an important explanation for the hysteresis of the stability failure of the tunnel-slope system. The results have important guiding significance for the stability of the tunnel-slope system during construction.