Landslide susceptibility assessment has become the focus of geological disaster research to strengthen disaster prevention and mitigation. Landslide disasters frequently occur in the Hubei section of the Three Gorges Reservoir Area (TGRA), with some potential landslides located along the highway, which brings risks to highway engineering, maintenance and transportation. In this paper, a comprehensive landslide susceptibility evaluation indicator framework with three dimensions and 12 factors was established, and an integrated approach was applied to evaluate the landslide susceptibility level, which combined weights-of-evidence model, seven clustering algorithms, three quality evaluation indices and the elbow method. To validate the effectiveness of the methods, five objective measures were employed for evaluation. The 69 samples along the highway were used for training, and another 30 samples were collected for validation. The results showed that the landslide susceptibility level of potential landslides can be effectively predicted by K-means algorithm. It was found that the landslide susceptibility for each cluster had significant differences, which were mainly reflected in natural induced factors, followed by human induced factors, while the slope structure showed little differences; the areas with low landslide susceptibility appeared sheet distribution, while the areas with high landslide susceptibility showed zonal distribution along the Yangtze River and its tributaries. This study developed a comprehensive indicator system and method for landslide susceptibility assessment along highways and provided a reference for the risk evaluation and prevention management.

滑坡敏感性评估已成为地质灾害研究的重点，加强防灾减灾。三峡库区湖北段滑坡灾害频发，部分潜在滑坡位于公路沿线，给公路工程、养护和运输带来风险。本文建立了一个3维12个因素的综合滑坡易感性评价指标框架，采用综合方法对滑坡易感性水平进行评价，该方法结合了证据权重模型、7种聚类算法、3种质量评价指标。和肘法。为了验证这些方法的有效性，采用了五个客观措施进行评估。高速公路沿线的69个样本用于训练，另外收集了30个样本进行验证。结果表明，K-means算法可以有效预测潜在滑坡的滑坡敏感性水平。发现各聚类的滑坡敏感性存在显着差异，主要体现在自然诱发因素，其次是人为诱发因素，边坡结构差异不大；滑坡易发区呈片状分布，滑坡易发区沿长江及其支流呈带状分布。