This research examines how rainfall and mining affect the slope damage resulting from the transition from open-pit mining to underground mining. Using an unmanned aerial vehicle (UAV), the Huangniu slope of the Dexing Copper Mine was fully characterized, and experiments were conducted on rock samples from appropriate sites. First, the mechanical properties of the samples were measured. Then, the parameters of the similarity simulation experiments were derived based on the similarity theory. Subsequently, the rainfall, rock slope, data acquisition, and monitoring systems were designed. Finally, the rock mass failure with different slope angles was analyzed, and the deformation and damage patterns under the coupling effect were obtained. The results show that rainfall increases pore water pressure and moisture content. Rainfall and slope-slip water have more of an impact on the open-pit platform. The pore water pressure values on the upper rock mass rise faster than inside it. In the open-pit mining stage, the rock mass shifts slightly to the upper left. In the room mining stage, vertical fractures and goaf sinking occur. The fractures above the mine form a semi-ellipse. In the pillar mining stage, overlying rock displacement is evident and fractures persist. In the continuous pillar mining stage, the overlying rock collapses. The 65° slope model was the most damaged, while the 55° slope model was the least damaged. The results also suggest that the UAV guides sample selection.

Computational models enable accurate, timely prediction of landslides based on the monitoring data on-site as the development of artificial intelligence technology. The most existing prediction methods focus on finding a single prediction algorithm with excellent performance or an integrated and efficient hyperparameter optimization algorithm with a highly accurate regression prediction algorithm. In order to break through the limitation of generalization of prediction models, this paper proposes an ensemble model that combines deep learning algorithms, with a stacking framework optimized with the sliding window method. Multiple deep learning algorithms are set as the first layer of the stacking framework, which is optimized with the sliding window method to avoid confusion in the time order of datasets based on time series analysis. The Shengjibao landslide in the Three Gorges Reservoir is used as a case study. First, the cumulative displacement is decomposed into a trend and a periodic term using a moving average method. A single-factor and a multi-factor superposition model based on multiple deep learning algorithms are used to predict the trend and periodic term of the displacement, respectively. Finally, the predicted values of the trend and periodic terms are added to obtain the total predicted landslide displacement. For monitoring point ZK2-3, the values of RMSE and MAPE of the total displacement prediction with the stacking model are 15.93 mm and 0.54%, and the values of RMSE and MAPE of the best-performing individual deep learning model are 20.00 mm and 0.64%. The results show that the stacking model outperforms other models by combining the advantages of each individual deep learning algorithm. This study provides a framework for integrating landslide displacement prediction models. It can serve as a reference for the geological disaster prediction and the establishment of an early warning system in the Three Gorges Reservoir Area.

The safety production of gold, silver, copper, and other important metals is seriously threatened in the process of mining from open-pit to underground due to various factors such as infiltration caused by rainfall and unloading during mining. Furthermore, the current situation of open-pit mining in an increasing number of mines presents a high and steep terrain, which poses significant security risks. Accordingly, it is of great practical significance to investigate the failure mechanism of high-slope angles to ensure the long-term safe mining of mines, considering factors such as rainfall infiltration and excavation unloading. In this study, the slope failure of high-slope angles (45°, 55°, and 65°) under rainfall-mining coupling was analyzed using the discrete element MatDEM numerical simulation software. Herein, the stress distribution, failure characteristics, and energy conversion of the model were simulated under different slope angles to analyze the failure mechanism at each stage. The simulation results show that the damage scale is smallest at 55° and largest at 65°. This indicates that setting the slope angle to 55° can reduce the risk of slope instability. Moreover, the reduction of elastic potential energy during the mine room mining stage is similar to that of mechanical energy. During the pillar mining stage, stress is concentrated in each goaf, resulting in a greater reduction in mechanical energy compared to elastic potential energy. Finally, after the completion of the continuous pillar mining stage, stress becomes concentrated in the failure area, and the effect of the slope angle on mechanical energy reduction becomes evident after the complete collapse of the model.

This research examines how rainfall and mining affect the slope damage resulting from the transition from open-pit mining to underground mining. Using unmanned aerial vehicle (UAV), the Huangniu slope of Dexing Copper Mine was fully characterized, and experiments were conducted on rock samples from appropriate sites. First, the mechanical properties of the samples were measured. Then, the parameters of the similarity simulation experiments were derived based on the similarity theory. Subsequently, the rainfall, rock slope, data acquisition and monitoring systems were designed. Finally, the rock mass failure with different slope angles was analyzed, and the deformation and damage patterns under the coupling effect were obtained. The results show that rainfall increases pore water pressure and moisture content. Rainfall and slope-slip water have more impact on the open-pit platform. The pore water pressure values on the upper rock mass rise faster than inside. In the open-pit mining stage, the rock mass shifts slightly to the upper left. In the room mining stage, vertical fractures and goaf sinking occur. The fractures above the mine room form a semi-ellipse. In the pillar mining stage, the overlying rock displacement is evident, and fractures persist. In the continuous pillar mining stage, the overlying rock collapses massively. The 65° slope model is the most damaged, while the 55° slope model is the least damaged. The results also suggest that the UAV guides sample selection.

Rock fatigue damage and instability during failure processes involve continuous initiation, propagation, and coalescence of internal cracks accompanied by energy exchange with surroundings. Energy-based analysis provides fundamental insights into damage accumulation and transformation mechanisms. This paper systematically review the research progress on energy evolution patterns under various influencing factors, microstructural transformation mechanisms during damage development, and associated macroscopic mechanical behaviors. It synthesizes achievements in energy-based strength criteria, fatigue damage evolution models, constitutive relationships, and engineering stability assessments. Current limitations are identified, and future directions are proposed: 1) Investigating energy evolution mechanisms under multi-field coupling conditions; 2) Developing comprehensive energy-driven damage evolution laws and constitutive models; 3) Establishing energy-based fatigue life prediction frameworks for rock engineering applications. These advancements aim to enhance theoretical understanding and practical reliability in rock mechanics analysis.