Automated recognition of rock discontinuity in underground engineering using geometric feature analysis

Discontinuities in rock masses critically impact the stability and safety of underground engineering. Mainstream discontinuities identification methods, which rely on normal vector estimation and clustering algorithms, suffer from accuracy degradation, omission of critical discontinuities when orientation density is unevenly distributed, and need manual intervention. To overcome these limitations, this paper introduces a novel discontinuities identification method based on geometric feature analysis of rock mass. By analyzing spatial distribution variability of point cloud and integrating an adaptive region growing algorithm, the method accurately detects independent discontinuities under complex geological conditions. Given that rock mass orientations typically follow a Fisher distribution, an adaptive hierarchical clustering algorithm based on statistical analysis is employed to automatically determine the optimal number of structural sets, eliminating the need for preset clusters or thresholds inherent in traditional methods. The proposed approach effectively handles diverse rock mass shapes and sizes, leveraging both local and global geometric features to minimize noise interference. Experimental validation on three real-world rock mass models, alongside comparisons with three conventional directional clustering algorithms, demonstrates superior accuracy and robustness in identifying optimal discontinuity sets. The proposed method offers a reliable and efficient tool for discontinuities detection and grouping in underground engineering, significantly enhancing design and construction outcomes.