Abstract Although numerical methods based on strength reduction are becoming popular in slope stability analysis, they fail to provide a distinct critical slip surface and only provide a shear band. The widely used visualization techniques for defining the critical slip surface are susceptible to subjective judgment and are inefficient for batch analysis and three-dimensional analysis. When a slope fails, the displacements on the two sides of the critical slip surface will be substantially different. Based on this observation, an automatic identification method for locating the critical slip surface is proposed. The k-means clustering algorithm is first applied to automatically separate the nodal displacements into two categories representing the sliding mass and the stable block. Then, the scatters near the separation surface are obtained by constructing the alpha shape of the sliding mass. Finally, the critical slip surface is obtained by fitting the extracted scatters. A homogeneous slope, a slope with a thin weak layer and a real landslide are used to test the effectiveness of the proposed method. The results show that the proposed method can automatically and accurately identify two-dimensional and three-dimensional critical slip surfaces.

中文翻译：

---

边坡临界滑移面自动识别

摘要 虽然基于强度折减的数值方法在边坡稳定性分析中越来越流行，但它们不能提供明显的临界滑移面，只能提供剪切带。用于定义临界滑动面的广泛使用的可视化技术容易受到主观判断的影响，并且对于批量分析和三维分析效率低下。当一个边坡失效时，临界滑移面两侧的位移会有很大的不同。基于这一观察，提出了一种定位临界滑移面的自动识别方法。首先应用 k-means 聚类算法将节点位移自动分为两类，分别代表滑动块和稳定块。然后，分离面附近的散射是通过构造滑动质量的 alpha 形状获得的。最后，通过拟合提取的散射点获得临界滑移面。以均质边坡、薄弱层边坡和真实滑坡为例，验证了该方法的有效性。结果表明，该方法能够自动准确地识别二维和三维临界滑移面。