Automatic identification method for discontinuities in underground rock masses based on the AHC method

Abstract

Discontinuities in underground rock masses are critical for engineering stability and construction safety. However, under complex geological conditions, existing method are often hindered by limited recognition and clustering accuracy, particularly in the presence of uneven data distribution or noise. A virtual multi-camera system is utilized to acquire rock surface imagery, and high-precision 3 D point cloud models are generated using Structure-from-Motion and Multi-View Stereo(SFM-MVS) techniques. This approach effectively mitigates the safety risks associated with manual measurements and enhances data acquisition efficiency.To process the point cloud data, a self-adaptive region-growing algorithm is developed, enabling accurate identification of independent structural planes under intricate geological conditions. Additionally, an Adaptive Hierarchical Clustering(AHC) algorithm is proposed based on statistical analysis and the Fisher distribution, allowing the optimal number of structural plane groups to be determined automatically. This addresses the limitations of conventional method that rely on predefined cluster numbers or thresholds.The proposed framework was validated using a dataset comprising 639000 points, which was successfully processed in 37 seconds, demonstrating high computational efficiency. The AHC algorithm achieved a recognition accuracy of 96.43%, outperforming conventional method by 8.5% and 14.3%, respectively. Furthermore, the self-adaptive region-growing algorithm exhibited strong completeness in identifying complex boundary regions such as sharp edges and fissure intersections.

Keywords

underground cavern / rock mass discontinuities / 3 D point cloud / optimal attitude grouping / hierarchical iterative clustering algorithm