Slope stability is one of the most important topics of engineering geology with a background of more than 300 years. So far, various stability assessment techniques have been developed which include a range of simple evaluations, planar failure, limit state criteria, limit equilibrium analysis, numerical methods, hybrid and high-order approaches which are implemented in two-dimensional (2D) and three-dimensional (3D) space. In the meantime, limit equilibrium methods due to their simplicity, short analysis time, coupled with probabilistic and statistics functions to estimate the safety factor (F.S), probable slip surface, application on different failure mechanisms, and varied geological conditions has been received special attention from researchers. The presented paper provides a review to limit equilibrium methods used for discontinuous rock slope stability analyses with different failure mechanisms of natural and cut slopes. The article attempted to provide a systematic review for rock slope stability analysis outlook based on limit equilibrium approaches.

This study performs a GIS-based landslide susceptibility assessment using a convolutional neural network, CNN, in a study area of the Gorzineh-khil region, northeastern Iran. For this assessment, a 15-layered CNN was programmed in the Python high-level language for susceptibility mapping. In this regard, as far as the landside triggering factors are concerned, it was concluded that the geomorphologic/topographic parameters (i.e., slope curvature, topographical elevation, slope aspect, and weathering) and water condition parameters (hydrological gradient, drainage pattern, and flow gradient) are the main triggering factors. These factors provided the landside dataset, which was input to the CNN. We used 80% of the dataset for training and the remaining 20% for testing to prepare the landslide susceptibility map of the study area. In order to cross-validate the resulting map, a loss function, and common classifiers were considered: support vector machines, SVM, k-nearest neighbor, k-NN, and decision tree, DT. An evaluation of the results of the susceptibility assessment revealed that the CNN led the other classes in terms of 79.0% accuracy, 73.0% precision, 75.0% recall, and 77.0% f1-score, and, hence, provided better accuracy and the least computational error when compared to the other models.

Collapse of the residual soil over bedrock cavities often occurs during construction in karst terrain, particularly when the thickness of the residuum is reduced during excavation. Even if an estimate of the strength of the residual soil is known, uncertainty with respect to the size/geometry of the subterranean voids makes a detailed analysis difficult, and straightforward methods to check the stability are needed. In this study, numerical analyses were performed to develop a stability chart expressed in terms of a dimensionless stability number and the geometry of a potential void in the residual soil. The stability charts include the effect of friction angle, and are also developed to allow the investigation of the effect of the inverted strength profile typically observed in karst terrain. Such stability numbers may be useful to estimate the stability of a given site based on the expected thickness of the soil overburden and the likely range of anticipated soil void diameters.