A new algorithm for high-speed identification of discontinuities on large-scale rock outcrop: A case study in Jinsha River suture zone

Automatic identification of discontinuities is a key focus in rock slope research. Conventional methods typically target small areas, which limits efficiency and applicability for complex discontinuities in large-scale rock slopes. This study uses multi-angle unmanned aerial vehicle (UAV) nap-of-the-object photogrammetry to construct a high-definition three-dimensional (3D) point cloud model of the slope. The edge-first connection algorithm identifies all edge points of discontinuities in the point cloud and completes recognition through simple connection analysis. This method avoids the complex calculations required for sequentially identifying discontinuity edges in conventional methods and achieves significant acceleration through algorithm optimization and parallel computation support. Based on this algorithm, the RockDiscontinuity Identification (RD ID) software is developed and applied to identify numerous highly disordered discontinuities on the Xulong slope in the Jinsha River suture zone. Processing tens of millions of point clouds within approximately 2 h demonstrates exceptional computational efficiency. The automatic algorithm accurately identifies nearly 80% of planar discontinuities, with orientations and trace lengths closely matching manual results, highlighting its potential for large-scale rock outcrop applications. Comparisons with region growing algorithms further emphasize its effectiveness and accuracy. However, the algorithm struggles to identify linear discontinuities, which are a major source of error. Additionally, high roughness and smooth edges of discontinuities affect recognition accuracy, indicating areas for further improvement.