# Risk Analysis of Service Slope Hazards for Highways in the Mountains Based on ISM-BN
## 5 Results and Discussion
They have the attributes of risk sources and can be pre-empted with targeted risk prevention and control measures. As can be seen from Figure 4, the posterior probability of root nodes \(X_{1}\), \(X_{3}\), and \(X_{6}\) was larger, respectively, at 28%, 66%, and 63%, which were risk factors leading to the risk of service slope hazards at an extremely high level of probability of a larger risk factor, significantly higher than the risk level of other root nodes, which requires special attention in risk management and control. The root nodes \(X_{4}\), \(X_{5}\), and \(X_{21}\) exhibit the highest sensitivity indices, specifically 0.1015, 0.2935, and 0.1553 in sequence. This indicates that a slight adjustment to the grading levels of these sensitivity factors would have a significant impact on the magnitude of in-service slope disasters. The influence intensities of \(X_{22}\)\(\rightarrow\)\(X_{24}\), \(X_{18}\)\(\rightarrow\)\(X_{19}\), and \(X_{17}\)\(\rightarrow\)\(X_{19}\) were conspicuously greater than those of other risk transmission links. These particular links exert a more significant impact on the overall risk network. Consequently, the risk level can be effectively diminished by controlling the risk factors situated at both end termini of these two links, thereby severing these links. Based on the above analyses, it is found that among the risk influencing factors of root nodes, the engineering geological conditions (\(X_{1}\)), micro-topography and landform (\(X_{3}\)), and the lowest monthly average temperature (\(X_{6}\)) had the greatest impact on disasters of service soil cut slope. These particular elements thus stand out as the key risk factors that lead to slope disaster events. Annual precipitation (\(X_{4}\)), daily maximum rainfall (\(X_{5}\)), and the possible damage degree of in-road facilities (\(X_{21}\)) held a relatively significant potential influence over the risk of slope disasters. The above risk diagnosis results were consistent with the theoretical reality of highway slope engineering, verifying the feasibility and effectiveness of the model. To further verify the feasibility and effectiveness of the model, the information variables related to past real disaster events of service soil cut slopes on mountainous area expressways in Anhui Province were introduced into the BN model for scenario analysis. Taking the cut slope K466 + 000 on the downlink of the Tongling to Chizhou Expressway section as an example, the specific information of event factor variables is shown in Table 8.  
The highway was completed and opened to traffic at the end of 2006, with excellent construction quality and good operation. The project is located in the transition zone from the Yangtze River floodplain to low hills. It has a two-level slope. The first-level slope was protected by a mortar rubble facing wall with a slope height of 5.5 m, and the second-level slope was protected by grass planting, with a slope height of 10 m. The subgrade side ditch was a blind drain, the slope covered with silty clay, which is expansive, and the vegetation  
Figure 5: Results of BN backward inference, sensitivity analysis, and optimal causal chain analysis. on the slope grows ordinarily. There is a high-voltage power tower outside the top of the slope. In July 2016, eroded by the extraordinary rainstorm, the top of the slope slid down. The landslide buried the emergency lane on the roadway, and the traffic was not interrupted for the time being. This section was identified as a high-risk slope. The relevant factors of slope disaster risk were subjected to grade normalization processing and then imported into the Bayesian network model for quantitative analysis. The running results are shown in Figure 6. Obviously, the probability that the slope has a high hazard risk is higher at 33%, which is consistent with the results of the risk at the project site, and the reasoning process is clear and straightforward.