# Risk Analysis of Service Slope Hazards for Highways in the Mountains Based on ISM-BN
## 5 Results and Discussion
In engineering  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Node**} & \multirow{2}{*}{**Status**} & \multicolumn{4}{c}{**Evalution Guide**} & \multirow{2}{*}{**Prior**} & \multirow{2}{*}{**Node**} & \multirow{2}{*}{**Status**} & \multicolumn{4}{c}{**Evalution Guide**} & \multirow{2}{*}{**Prior**} \\ \cline{2-2} \cline{6-11}  & & & & & & & & & & & & & & & \\ \hline \multirow{3}{*}{\(X_{1}\)} & R1 & 3 & 3 & 4 & 4 & 4 & 0.3203 & \multirow{3}{*}{\(X_{13}\)} & R2 & 4 & 4 & 3 & 4 & 3 & 0.307 \\  & R2 & 3 & 4 & 4 & 5 & 5 & 0.4503 & \multirow{3}{*}{\(X_{13}\)} & R2 & 4 & 5 & 5 & 5 & 0.3007 \\  & R3 & 3 & 3 & 4 & 4 & 4 & 0.3207 & \multirow{3}{*}{\(X_{21}\)} & R1 & 3 & 3 & 2 & 2 & 2 & 0.0218 \\  & R3 & 3 & 3 & 2 & 2 & 3 & 0.0200 & & \multirow{3}{*}{\(X_{17}\)} & R2 & 4 & 4 & 3 & 3 & 3 & 0.2705 \\ \hline \multirow{3}{*}{\(X_{4}\)} & R1 & 3 & 3 & 2 & 2 & 0.0507 & \multirow{3}{*}{\(X_{18}\)} & R1 & 3 & 3 & 3 & 2 & 2 & 0.0203 \\  & R2 & 4 & 4 & 4 & 4 & 4 & 0.3009 & \multirow{3}{*}{\(X_{21}\)} & R2 & 8 & 8 & 5 & 5 & 5 & 0.2720 \\  & R3 & 5 & 5 & 6 & 6 & 6 & 0.5478 & \multirow{3}{*}{\(X_{22}\)} & R3 & 4 & 4 & 3 & 3 & 3 & 0.2533 \\  & R4 & 2 & 2 & 2 & 3 & 3 & 0.0703 & \multirow{3}{*}{\(X_{20}\)} & R1 & 3 & 3 & 3 & 4 & 4 & 0.1985 \\ \hline \multirow{3}{*}{\(X_{5}\)} & R1 & 2 & 2 & 2 & 3 & 3 & 0.0407 & \multirow{3}{*}{\(X_{20}\)} & R2 & 6 & 6 & 5 & 4 & 5 & 0.4737 \\  & R2 & 4 & 3 & 3 & 3 & 4 & 0.2456 & \multirow{3}{*}{\(X_{23}\)} & R3 & 4 & 3 & 3 & 2 & 0.1985 \\  & R3 & 5 & 6 & 6 & 5 & 0.5484 & \multirow{3}{*}{\(X_{21}\)} & R1 & 3 & 2 & 2 & 3 & 3 & 0.0206 \\  & R4 & 2 & 2 & 3 & 3 & 3 & 0.0407 & \multirow{3}{*}{\(X_{21}\)} & R2 & 4 & 4 & 3 & 3 & 3 & 0.2128 \\  & R5 & 2 & 2 & 2 & 3 & 3 & 0.0407 & \multirow{3}{*}{\(X_{22}\)} & R1 & 4 & 4 & 4 & 4 & 4 & 0.3046 \\  & R1 & 5 & 6 & 6 & 6 & 5 & 0.6279 & \multirow{3}{*}{\(X_{21}\)} & R1 & 4 & 4 & 5 & 5 & 5 & 0.3020 \\  & R2 & 3 & 4 & 3 & 4 & 4 & 0.2027 & \multirow{3}{*}{\(X_{22}\)} & R1 & 2 & 2 & 2 & 3 & 1 & 0.0400 \\  & R3 & 3 & 3 & 4 & 4 & 4 & 0.1646 & \multirow{3}{*}{\(X_{22}\)} & R2 & 3 & 3 & 2 & 2 & 0.1186 \\ \hline \multirow{3}{*}{\(X_{6}\)} & R1 & 2 & 2 & 1 & 3 & 0.0404 & \multirow{3}{*}{\(X_{21}\)} & R1 & 4 & 4 & 4 & 3 & 3 & 0.2317 \\  & R2 & 4 & 4 & 3 & 3 & 3 & 0.2178 & \multirow{3}{*}{\(X_{22}\)} & R1 & 6 & 5 & 6 & 5 & 0.5546 \\ \cline{1-1}  & R3 & 5 & 6 & 6 & 5 & 5 & 0.0404 & \multirow{3}{*}{\(X_{23}\)} & R1 & 2 & 2 & 3 & 3 & 0.04  
practice, the slopes of operational expressways in mountainous areas are so numerous that it is infeasible to conduct special designs on a case-by-case basis. Instead, the majority of them are constructed in accordance with engineering experience. Consequently, both the overall design quality and construction quality generally remain at a merely qualified level and fail to attain high quality. In addition, the probability of the slope maintaining a generally stable condition is 60%. This aligns with the widely recognized fact that as the service time of the slopes along operational expressways in mountainous regions extends, the reinforcement corrosion and concrete deterioration within the slope support and drainage structures take place. This, in turn, leads to the overall performance of the slope entering a state of gradual decline. The model graphically describes the quantitative mapping relationship between various influencing factors, presenting a clear and vivid picture of how they interact and relate to one another. To further diagnose the risk incentives, the reverse reasoning, sensitivity analysis, and influence intensity analysis functions of the Bayesian network were utilized to quantitatively study the slope disaster risk factors and risk transmission links and calculated the posterior probabilities, sensitivities, and intensities of risk transmission links of non-leaf nodes under the \"very high\" risk level, and the results are shown in Figure 5.  
Figure 4: Bayesian network (BN) for service slope disaster risk. Root node factors are the focus of the risk diagnostic process.