Semi-automatic recognition of rock mass discontinuity based on 3D point clouds

Abstract

Information on rock mass discontinuities is crucial for rock mass stability analysis. Due to the low efficiency, incompleteness, and potential risk of the traditional compass methods in measuring discontinuities, three-dimensional light detection, ranging, and other remote sensing methods have become essential. In this study, voxel filtering was used to subsample a point cloud so that its feature points were retained while reducing the computational load. An improved regional growing (RG) algorithm was then used to extract rock mass discontinuities. A software Geocloud v1.0 was developed based on the proposed method to semi-automatically recognize discontinuities. Additionally, two groups of sensitivity experiments were performed to analyze the influence of different numbers of nearest neighbors and maximum RG angles on the extraction of discontinuities. Results showed that most of the discontinuities could be accurately recognized with different thresholds. Furthermore, the accuracy of the proposed method was verified by real geometries, on a real highway slope, and in a natural quarry. Finally, the efficiency of the proposed method was proven using comparative computational experiments.