# Case Study and Risk Assessment of Water Inrush Disaster in Qingdao Metro Line 4
## 4 Water Inrush Risk Assessment  
### Evaluation Index System for Water Inrush Risk  
The main risk factors inducing water inrush disasters in tunnels include geological structure, landform, tunnel and surrounding rock conditions, and hydrological conditions [44]. Based on the geological characteristics of the Qingdao area, which is soft at the top and hard at the bottom, and combined with the actual engineering problems encountered in the excavation process of the Qingdao subway, the water inrush risk evaluation index system for subway tunnels in Qingdao area was established, as shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Level 1 Indicators**} & \multirow{2}{*}{**Secondary Indicators**} & \multicolumn{5}{c}{**Levels of Danger**} \\ \cline{3-6}  & & **C\({}_{4}\) (Low Risk)** & **C\({}_{3}\) (Medium Risk)** & **C\({}_{2}\) (High Risk)** & **C\({}_{1}\) (Very High Risk)** \\ \hline \multirow{6}{*}{Tunnel and surrounding rock conditions I\({}_{1}\)} & Tunnel excavation width I\({}_{11}\) & \(<\)8.5 & [8.5, 12) & [12, 14) & \(\geq\)14 \\  & Tunnel depth I\({}_{12}\) & \(<\)10 & [10, 30) & [30, 50) & \(\geq\)50 \\ \cline{1-1}  & Rock integrity I\({}_{13}\) & whole & broken & broken & extremely broken \\ \cline{1-1}  & Rock saturated uniaxial & \(>\)60 & (30, 60) & (15, 30) & \(\leq\)15 \\ \cline{1-1}  & compressive strength I\({}_{14}\) & \multirow{2}{*}{underdeveloped} & \multirow{2}{*}{development} & \multirow{2}{*}{more developed} & \multirow{2}{*}{very developed} \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{6}{*}{Geological structure and surface factors I\({}_{2}\)} & Fault fracture zone width I\({}_{21}\) & \(<\)50 & [50, 100) & [100, 300) & \(\geq\)300 \\ \cline{1-1}  & Catchment area/\({}^{\circ}\)I\({}_{22}\) & \(<\)20 & [20, 40) & [40, 60) & \(\geq\)60 \\ \cline{1-1}  & & Topography I\({}_{23}\) & flat & slope & steep terraces, valleys & denuded mounds, eroded plains \\ \cline{1-1}  & Composite ratio of soft and hard formations/\({}^{\circ}\)I\({}_{24}\) & \(<\)25 & [25, 50) & [50, 75) & \(\geq\)75 \\ \hline \multirow{6}{*}{Hydrological conditions I\({}_{3}\)} & Water richness of groundwater I\({}_{31}\) & no water & slightly watery & watery & rich in water \\ \cline{1-1}  & Elevation difference of groundwater I\({}_{32}\) & \(<\)10 & [10, 30) & [30, 60) & \(\geq\)60 \\ \cline{1-1}  & Permeability coefficient I\({}_{33}\) & \(<\)0.01 & [0.01, 1) & [1, 10) & \(\geq\)10 \\ \cline{1-1}  & Average monthly rainfall I\({}_{34}\) & \(<\)60 & [60, 80) & [80, 100) & \(\geq\)100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Risk assessment index system for tunnel water inrush in the Qingdao area. Figure 17: Negative pressure zone. The training data were dimensionally reduced by grey correlation analysis. Taking the risk level as the parent sequence and the quantitative index value as the subsequence, we calculated the gray correlation coefficient between the subsequence and the indicators of the parent sequence and averaged them to obtain the correlation degree of each indicator, as shown in Table 6. Before calculation, all data were nondimensionalized using the initial value method to reduce analytical errors. The indicators were sorted according to their correlation degree, and the first 12 index values with high correlation degree were selected as input values. The 12 sets of sample data were input as training data, and the actual water inrush risk levels (low risk, medium risk, high risk, and very high risk) were represented by numbers 1, 2, 3, and 4. In order to ensure the accuracy of the model, the optimal number of clusters was set to k = 4, and the maximum number of iterations when selecting the center for PAM clustering was 600 times. The improved RBF neural network was trained by the gradient descent method, the err-goal was 0.005, and the maximum number of iterations was set to 10,000.