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.

Underwater terrain-aided navigation (TAN) can obtain high-precision positioning independently and autonomously under the conditions of a communication rejection space, which is an important breakthrough for the autonomous and refined operation of deep-sea autonomous underwater vehicles near the seabed. Although TAN originated in the aviation field, the particularity of the underwater physical environment has led to the formation of a different theoretical and technical system. In this article, the application background, operating principles, and most important technical aspects of underwater TAN are introduced. Then, the relevant algorithms involved in the two main modules (the terrain-aided positioning module and the iterative filtering estimation module) of the underwater TAN are reviewed. Finally, other cutting-edge issues in the field of underwater TAN are summarized. The purpose of this article is to provide researchers with a comprehensive understanding of the current research status and possible future developments in the TAN field.

Slopes facing reservoir water often collapse, affecting reservoir capacity. It is important to understand the factors affecting such collapses and predict the locations of collapse after impoundment for practical purposes. However, the mechanism of bank collapse in mountainous areas after reservoir impoundment remains unclear. To address this issue, field geotechnical property surveys, laboratory tests, and numerical simulations were employed to evaluate bank collapse susceptibility. The results showed that (i) bank collapse mainly occurs in slopes with a small cohesion and internal friction angle of less than 30°; (ii) the main effects of wave action on bank collapse are scour, erosion, and cyclic impact on the bank slope: wave erosion occurs in the bank slope with a wave height of less than 1.0 m, cohesion of less than 90 kPa, and slope angle of less than 30°, and wave impaction mainly occurs at the area with a wave height of more than 2.0 m and a slope angle of more than 30°; (iii) for valley reservoirs, softening of water on the slope rock and soil mainly reduces cohesion, which commonly occurs within 30 days; the cyclic action of wave impact load mainly damages the structure and reduces the internal friction angle. The influence of waves on bank collapse is mainly related to the wave height and wave velocity. The antiwave impact broken energy of rock and soil is an important index to evaluate whether bank collapse occurs in the long term, which is related to the strength, structure, and impact resistance. The present research is helpful to better understand the mechanism and evolution process of reservoir bank collapse.