Rockslides along a stepped failure surface have characteristics of stepped deformation characteristic and it is difficult to predict the failure time. In this study, the deformation characteristics and disaster prediction model of the Fengning granite rockslide were analyzed based on field surveys and monitoring data. To evaluate the stability, the shear strength parameters of the sliding surface were determined based on the back-propagation neural network and three-dimensional discrete element numerical method. Through the correlation analysis of deformation monitoring results with rainfall and blasting, it is shown that the landslide was triggered by excavation, rainfall, and blasting vibrations. The landslide displacement prediction model was established by using long short-term memory neural network (LSTM) based on the monitoring data, and the prediction results are compared with those using the BP model, SVM model and ARMA model. Results show that the LSTM model has strong advantages and good reliability for the stepped landslide deformation with short-term influence, and the predicted LSTM values were very consistent with the measured values, with a correlation coefficient of 0.977. Combined with the distribution characteristics of joints, the damage influence scope of the landslide was simulated by three-dimensional discrete element, which provides decision-making basis for disaster warning after slope instability. The method proposed in this paper can provide references for early warning and treatment of geological disasters.

The cumulative displacement-time curve is the most common and direct method used to predict the deformation trends of landslides and divide the deformation stages. A new method based on the inverse logistic function considering inverse distance weighting (IDW) is proposed to predict the displacement of landslides, and the quantitative standards of dividing the deformation stages and determining the critical sliding time are put forward. The proposed method is applied in some landslide cases according to the displacement monitoring data and shows that the new method is effective. Moreover, long-term displacement predictions are applied in two landslides. Finally, summarized with the application in other landslide cases, the value of displacement acceleration, 0.9 mm/day², is suggested as the first early warning standard of sliding, and the fitting function of the acceleration rate with the volume or length of landslide can be considered the secondary critical threshold function of landslide failure.

The purpose of this study is to identify the leading causes of slope instability along a local highway in Anhui, People’s Republic of China. As part of the east expansion project, the mountain range will be excavated to create a two-way, nearly 30 m wide highway. The site’s topography consists of a hill with palm-shaped faces carved from limestone running along its sides. The geological characteristics and slope stability of the research area highlight the possibility of slope failure along both sides of the roadway. Slope stability analysis was performed in order to determine the failure mechanism and create a stable slope. Initial slope characterization and shear properties of the rock were determined by means of fieldwork and laboratory analysis. By causing wedging failure and toppling collapse, the bedding joints and discontinuity orientations increase instability, as determined by a kinematic analysis performed with DIP.6 software. The Limit Equilibrium Method (LEM) of analysis is presented in the software Slide 6.020 to illustrate the instability of the slope. The unstable condition of the slopes was determined using empirical methods that were validated and enhanced by limit equilibrium analysis.