# Risk Analysis of Service Slope Hazards for Highways in the Mountains Based on ISM-BN
## 4 Construction of ISM-BN Model
### Constructing the BN Model of HSD Risk  
Construct the BN according to the following steps. Step 5: Form the Bayesian network  
Based on the arcs between the risk factors of the disaster system for service soil cut slopes of expressways in mountains obtained by ISM, the ISM hierarchical structure is transformed into a corresponding Bayesian network topology as the basis of the BN inference model. From Figure 2, it can be seen that the ISM forms a directed graph without redundant and circular arc links, which are not allowed in BN [37]. The nodes in the directed graph are transformed into the nodes of the Bayesian network topology. Step 6: Determine network parameters  
(1) Node status classification  
Firstly, the relevant variables are handled through fuzzy logic. According to the Specification for Risk Assessment of Geological Hazard (GB/T 40112) [38] and Census technical handbook for highway hazard-affected bodies in natural disaster comprehensive risk, with reference to the research of [PERSON] et al. [28] and [PERSON] et al. [39; 40], and combined with the opinions of experts, the node states are divided into at most five states, which are uniformly represented by \"R1\", \"R2\", \"R3\", \"R4\", and \"R5\", comprehensively considering the actual situation of factors and the parameter learning cost. The states are ranked from strong to weak depending on the intensity of their influence. The specific classification is shown in Table 2. These levels correspond to the states of the nodes in the BN topology to better reflect the level of slope disaster risk. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Node** & **R1** & **R2** & **R3** & **R4** & **R5** \\ \hline Engineering geological & & & & & \\ conditions \(X_{1}\) & Poor & Moderate & Simple & — & — \\ \hline Hydrological and & & & & & \\ hydrogeological & Poor & Moderate & Simple & — & — \\ conditions \(X_{2}\) & & & & & \\ \hline Micro-topography and & & & & & \\ landform \(X_{3}\) & & & & & \\ \hline Annual precipitation & & & & & \\ \(X_{4}\) (mm) & & & & & \\ \hline Daily maximum rainfall & & & & & \\ \(X_{5}\) (mm) & & & & & \\ \hline Lowest monthly average & & & & & \\ temperature \(X_{6}\) (\({}^{\circ}\)C) & & & & & \\ \hline Soil properties \(X_{7}\) & Clayey soil & Silt & Sand & Gravellly soil & — \\ \hline Soil compaction \(X_{8}\) & Loose & Slightly dense & Moderately dense & dense & — \\ \hline Soil moisture status \(X_{9}\) & Very wet & Wet & Slightly wet & — & — \\ \hline Shear strength \(X_{10}\) (\(\varphi^{\prime}/^{\circ}\)) & \(\varphi^{\prime}\leq 25\) & & & & \\ \hline Slope height \(X_{11}\) (m) & \(X_{11}\geq 20\) & & \(6\leq X_{11}<20\) & \(X_{11}<6\) & — & — \\ \hline Slope gradient \(X_{12}\) & \(X_{12}>10.9\) & & \(1{:}1{:}2\leq X_{11}<10.9\) & & \(1{:}1{:}5\leq X_{12}<1{:}1{:}2\) & \(X_{12}<1{:}1{:}5\) & — \\ \hline Slope disease history \
(2) Parameter learning  
Parameter learning of the Bayesian network (BN) is the process of determining the conditional probability table (CPT) of the network. Due to the lack of systematic statistical data in the field of highway service slopes in mountains, it is difficult to conduct ideal parameter learning for BN using machine learning. The paper established a BN model by means of a parametric learning method based on expert knowledge and historical experience. The BN model mainly relies on the prior probability values of root nodes. Considering that experts usually tend to give a fuzzy interval rather than an exact value when estimating the probability of an event occurring. Moreover, factors such as experts' knowledge reserves and subjective concepts can also lead to a certain degree of uncertainty in the given results. Therefore, it is determined by applying the IPCC ranking criteria [41] (as shown in Table 3) combined with the decision of the expert group. The prior probabilities of root nodes are characterized by inviting several experts to give fuzzy intervals.