A Computationally Efficient Approach to Automatically Extract Rock Mass Discontinuities from 3D Point Cloud Data

Geometric characterization of discontinuity planes in rock masses is an essential practice in rock engineering. Analysis of 3D point cloud data of exposed rock surfaces acquired by remote sensing techniques is becoming widespread due to its benefits such as quality and quantity of the data. In this study, we propose a computationally efficient workflow to extract planar discontinuities from point cloud data automatically. The proposed workflow includes voxel-grid downsampling as a preprocessing step to smooth the point cloud, forming a covariance matrix and applying singular value decomposition to estimate normal vectors, primary clustering to assign the normal vectors to a cluster, secondary clustering to separate individual discontinuity planes in each set, and plane fitting using RANSAC to extract the orientation and location of each discontinuity plane. The proposed workflow's ability to accurately extract discontinuities and calculate their orientations in a computationally efficient procedure was demonstrated through the analysis of two case studies of rock face point clouds.