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In the exploration tunnels of the Xiageliao landslide, slide failures were found not only inside the slip soil but also on the contact surface between the slip soil and the tuff. The undisturbed slip soil was collected from the exploration tunnel for testing to explore the pattern of slide failure occurrence. The surfaces of concrete blocks of different shapes were used to simulate slip soil–tuff contact surfaces of different roughness. The shear strength of these contact surfaces with different roughness was tested using a large-scale shear test system and analyzed through comparison with the shear strength of the slip soil. The test results showed that the interface roughness significantly affected the shear strength of the slip soil contact interface and determined the shear failure mode of the slip soil. When the roughness is below a certain level, the shear strength of the contact surface is less than the soil shear strength, and when the roughness reaches a certain level, the shear strength of the contact surface exceeds the strength of the slip soil itself. Thus, the strength of the slip soil itself sometimes differs from the strength of the slip surface. Based on the Moore–Cullen law, the test results found a formula for calculating the contact surface shear strength. These results provide some references for the risk evaluation of landslides. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) is significant for landslide risk assessment. However, there remains no consensus on which method is optimal for LSM. This study implements a dynamic approach to landslide hazard mapping by integrating spatio-temporal probability analysis with time-varying ground deformation velocity derived from the MT-InSAR (Multi-Temporal InSAR) method. Reliable landslide susceptibility maps (LSMs) can inform landslide risk managers and government officials. First, sixteen factors were selected to construct a causal factor system for LSM. Next, Pearson correlation analysis, multicollinearity analysis, information gain ratio, and GeoDetector methods were applied to remove the least important factors of STI, plan curvature, TRI, and slope length. Subsequently, information quantity (IQ), logistic regression (LR), frequency ratio (FR), artificial neural network (ANN), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) methods were performed to construct the LSM. The results showed that the distance to a river, slope angle, distance from structure, and engineering geological rock group were the main factors controlling landslide development. A comprehensive set of statistical indicators was employed to evaluate these methods' effectiveness; sensitivity, F1-measure, and AUC (area under the curve) were calculated and subsequently compared to assess the performance of the methods. Machine learning methods' training and prediction accuracy were higher than those of statistical methods. The AUC values of the IQ, FR, LR, BP-ANN, RBF-ANN, RF, SVM, and CNN methods were 0.810, 0.854, 0.828, 0.895, 0.916, 0.932, 0.948, and 0.957, respectively. Although the performance order varied for other statistical indicators, overall, the CNN method was the best, while the BP-ANN and RBF-ANN method was the worst among the five examined machine methods. Hence, adopting the CNN approach in this study can enhance LSM accuracy, catering to the needs of planners and government agencies responsible for managing landslide-prone areas and preventing landslide-induced disasters. [ABSTRACT FROM AUTHOR]