Intelligent joint mapping and hazard areas of open-pit slopes under complex geology: the Yanshan iron mine case

Rapid identification of hazardous areas is crucial for reducing landslide risks. To address this, this study proposes a hazard assessment method based on UAV oblique photography and automated structural surface identification, applied to hazard identification and stability analysis in the Yanshan open-pit iron mine. A millimeter-accuracy 3D surface model was constructed using UAV low-altitude slope-following flights. Geometric features of structural surfaces were extracted using a density-based clustering algorithm, and 3D stability analysis was conducted with Rocslope software to precisely identify high-risk areas and their failure modes. The analysis revealed that the joint density and connectivity in the northeastern and northern slopes are significantly higher than in the eastern slope, with wedge failure as the predominant failure mode in slopes, and most hazardous blocks having a thickness of less than 3 m. Compared with natural conditions, the proportion of hazardous areas increased from 5.4% to 7.3% under saturated and blasting conditions, further demonstrating the significant impact of water and blasting on slope stability. Meanwhile, the shotcrete reinforcement measures were adopted for hazardous areas in advance, improving the slope stability. The proposed methodology improves the precision and efficiency of slope hazard identification, providing reliable data and technical support for landslide risk assessment.