## 3 Case Study
### Collapse Risk Assessment Based on Advance Geological Forecast Data  
According to the Guide to Construction Safety Risk Assessment for Highway Bridge and Tunnel Projects, the risk level of tunnel collapse is divided into four levels: low (level I), moderate (level II), high (level III) and very high (level IV). Combined with the effective data available for advanced geological prediction, the tunnel collapse risk evaluation index system is established, as shown in Table 1. The uniaxial compressive strength of rock is a direct reaction of rock hardness. The smaller the value, the softer the rock and the greater the risk of collapse. The integrity coefficient of rock mass can be obtained by converting the longitudinal wave velocity of rock mass. The smaller the value, the more incomplete the rock and the greater the risk of collapse. The angle between the main structural plane and the tunnel axis describes the combination relationship between the tunnel axis and the main structural plane of the chamber. The larger the angle, the more unstable the chamber and the greater the risk of collapse. The discontinuous structural plane state reflects the state of the chamber control structural plane. The more serious the corrosion, the worse the nature of the filling, the worse the stability of the chamber and the greater the risk of collapse. There are four types of risk levels in the advanced geological forecast datasets, namely Level I, Level II, Level III and Level IV. Each risk level has four attributes, which are uniaxial compressive strength of rock, surrounding rock integrity factor, angle between the main structural surface and the cave axis and discontinuous structural surface state. In this study, a dataset of 100 tunnel collapse cases is collected to form a training dataset for tunnel advance geological forecasting. The four attribute values are used as inputs and the model parameters are obtained after the inverse cloud generator, as shown in Table 2. With the obtained model parameters, a forward cloud generator is created. The test samples are fed into the forward cloud generator to obtain the tunnel collapse risk assessment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Evaluation Indicators**} & \multicolumn{4}{c}{**Collapse Level**} \\ \cline{2-5}  & **I** & **II** & **III** & **IV** \\ \hline Uniaxial compressive strength of rock/MPa & 80–120 & 30–80 & 10–30 & 5–10 \\ Surrounding rock integrity factor & 0.75–1 & 0.45–0.75 & 0.2–0.45 & 0–0.2 \\ Angle between the main structural surface and the cave axis/\({}^{\circ}\) & 80–90 & 50–80 & 10–50 & 0–10 \\ Discontinuous structural surface state & 0–0.2 & 0.2–0.5 & 0.5–0.8 & 0.8–1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Indices and criterion for tunnel collapse risk assessment. Figure 2: Longitudinal section of the right line of Wenbishan Tunnel. The black line represents the ground line, the blue line represents the surface line, and the green line represents the tunnel. In the section YX223 + 473\(\sim\)YK224 + 073, samples are selected every 10 m, and a total of 60 cross-sections are selected as test samples. The Cloud Model for the four risk classes under the four attributes is shown in Figure 3. There are overlaps in level I and level II of the property \"uniaxial compressive strength of rock\", level III and level IV of the property \"integrity coefficient of surrounding rock\", and level I and level II of the property \"state of discontinuous structural surface\". According to Equation (3), the overlapping degree of unaxial compressive strength of rock is \(S_{A}[(I,II),(II,III),(III,IV)]=[0.38,\ 0,\ 0]\), the overlapping degree of surrounding rock integrity factor is \(S_{B}[(I,II),(II,III),(III,IV)]=[0,\ 0,\ 0.2]\), the overlapping degree of angle between the main structural surface and the cave axis is \(S_{C}[(I,II),(II,III),(III,IV)]=[0,\ 0,\ 0]\) and the overlapping degree of discontinuous structural surface state is \(S_{D}[(I,II),(II,III),(III,IV)]=[0.25,\ 0,\ 0]\).