# Risk Analysis of Service Slope Hazards for Highways in the Mountains Based on ISM-BN
## 1 Introduction  
According to the statistics of the \"Census of Highway and Waterway Bearing Bodies at Comprehensive Risk of Natural Disasters\" completed by China in 2022, more than 220,000 pieces of highway disaster risk data have been collected nationwide, and more than 30,000 km of disaster sections have been cumulatively disposed of along highways during the 13 th Five-Year Plan period [1]. Highways are linear projects with long spans and varying geological conditions along the route, thus forming many fills and cuts with varying conditions for subgrade slopes. The safety risk analysis of these slopes has always been one of the key issues in highway operation. In recent years, disasters such as collapses and landslides on highways have caused significant destruction to public and private property, traffic disruptions, and even casualties [2,3]. In July 2020, affected by heavy rainfall, the slope at the entrance of Zhuling tunnel on the Ningji Highway in the southern mountainous area of Anhui Province was washed away, blocking traffic for 3 h. In October 2021, heavy rains caused landslides and mudslides on many slopes of highways in Sichuan, disrupting highway traffic and resulting in significant economic losses [4]. In May 2024, a landslide occurred at the embankment slope of the Meizhou to Dapu Expressway, leading to the interruption of traffic in both directions, with a death toll as high as 48 people. This suggests that slope hazards along highways not only pose a significant hazard to access but also impede traffic, leading to excessive maintenance costs and even injuries. Obviously, it is of great significance to scientifically and effectively analyze the disaster risk of service slopes, establish corresponding risk evaluation models, objectively assess the current situation of service slopes, identify key influencing factors, and prevent and resolve major risks. BN is a widely applied Artificial Intelligence (AI) technology, often referred to as belief networks, probabilistic networks, or causal networks, and has been extensively utilized in various fields, including dam risk analysis, environmental management, and construction management [5,6]. Accordingly, the study comprehensively considers the geological and structural characteristics of slopes, environmental features, and management factors along the mountainous operational highways. Drawing from historical disaster maintenance data for multiple service slopes of mountain highways in Anhui Province, China, and incorporating expert opinions, the fishbone analysis method was used to identify 25 influencing factors. It is challenging to construct a BN model with unknown structures or ambiguous parameters in the absence of complete historical data. ISM is introduced from a system perspective in order to fully analyze the relationship between factors and subdivide the complex system layer by layer through calculation to form a logically clear hierarchical relationship structure. Then, the ISM is converted into a BN model to calculate the overall risk level of service slope disasters. Subsequently, the key influencing factors and causal chain of slope hazard risk are determined through backward reasoning, sensitivity analysis, and strength of influence. The research results effectively address the complex interrelationships among various factors and provide systematic technical support for the risk assessment, early warning, and prevention of slope disasters on mountainous operational highways. The remainder of the paper is organized as follows: Section 2 is the literature review. Section 3 carries out the analysis and selection of factors influencing the risk of slope hazards. Section 4 presents the risk analysis network construction and analysis method proposed.