Understanding the mechanical responses of layered rock masses at both macro and micro scales, particularly under diverse confining pressures and bedding directions, is crucial for evaluating their stability and optimizing resource extraction. This study employs PFC2D numerical models, calibrated with laboratory data from Xinjiang Barkol oil shale, to investigate how confining pressure and bedding direction control the mechanical properties of layered rock masses during biaxial compression. The results demonstrate distinct failure modes, shifting from splitting in Per bedding (beddings perpendicular to the loading direction) to shear-tension and shear-slip failures in inclined bedding. A U-shaped distribution of compressive strength across bedding directions is observed, with strength increasing under higher confining pressure. A novel microfracture connection algorithm is proposed to quantify microfracture parameters, such as quantity, length, and angle, shedding light on the complex microfracture evolution mechanisms. Fewer persistent microfractures in Par (i.e., beddings are parallel to the loading direction) and Per beddings explain their higher compressive strength compared to inclined bedding. Additionally, microfracture length evolution demonstrates a shift from brittle to ductile macro-failure as bedding direction changes. Microfractures primarily develop parallel to the loading direction, while confining pressure slightly affects microfracture characteristics. These findings establish a new framework for predicting the behavior of layered rock masses under complex loading, providing theoretical insights and practical guidance for engineering applications.

Discontinuities are critical factors that influence slope stability. While planar discontinuities can be reliably identified, linear discontinuities remain challenging to identify because of their concealed nature and limited surface exposure. This study introduces an improved neighborhood search algorithm that utilizes thermal infrared (TIR) images to identify linear discontinuities. The method identifies discontinuities based on their thermal characteristics, using temperature differences between intact rock and discontinuities. To evaluate the effectiveness of the algorithm, a typical high-steep rock slope in the upper Jinsha River region was selected. High-resolution visible and TIR images were acquired using an unmanned aerial vehicle (UAV), and the method was applied to identify and validate discontinuities. Comparison with the results of manual identification indicated a positive correlation between discontinuity scale and identification rate. For linear discontinuities exceeding 10 m in trace length, the identification rate reached 70.5%. The method also demonstrated potential advantages in capturing temperature difference boundaries caused by hot air movement and in delineating unstable areas, as well as open discontinuities and rockfalls. This research provides a new idea for the automatic identification of linear discontinuities in complex slope conditions and offers valuable insights for rock mass engineering evaluation.