The assessment of slope stability plays a critical role in the prevention and management of slope disasters. Evaluating the condition and stability of hazardous rock masses is essential for predicting potential collapses and assessing treatment effectiveness. However, conventional measurement techniques are inadequate in high slope areas, which lack sufficient spatial data to support subsequent calculations and analyses. Therefore, this paper presents a method for the early identification and evaluation of unstable rock masses in high slopes using Unmanned Aerial Vehicle (UAV) digital photogrammetry and geographic information technology. By considering nine evaluation indices including geology, topography, and induced conditions within the study area, weights for each index are determined through an analytic hierarchy process. A semi-automatic approach is then utilized to extract and analyze rock mass stability. The reliability of this early identification method is confirmed by applying the limit equilibrium principle. The findings reveal that 17.6% of dangerous rock masses in the study area fall into the unstable category (W4, W6, W10). This method effectively assesses slope rock mass stability while providing technical support for disaster monitoring systems, warning mechanisms, and railway infrastructure safety defense capability to ensure safe mountain railway operations.

The detection of subgrade distresses in ballastless track railways poses a formidable challenge due to the presence of steel interference caused by the unique characteristics of high-speed rail track slabs and the dense arrangement of the steel reinforcement mesh within them. Here, we aim to examine the influence of varying distribution patterns of steel reinforcement in ballastless tracks on the detection of subgrade distresses using ground-penetrating radar. Through a combination of on-site testing and forward modeling, this paper analyzes the interference of steel reinforcement on the detection of voids beneath the steel using electromagnetic waves. The research findings reveal that incident electromagnetic waves from the ground-penetrating radar experience attenuation near steel reinforcements, with only a fraction able to penetrate the surface layer and propagate into the subsurface through interstitial gaps between the reinforcing bars. Furthermore, this influence diminishes as the spacing between the reinforcing bars increases and the bar diameter decreases. When steel bars are distributed on the upper and lower layers, the detection results of the lower void are most significantly influenced by the interlocking of the steel bars in the two layers. These research results can offer theoretical and technical support for the detection of ailments in high-speed railway subgrades.