Open Collections

Daghigh, Hamid

University of British Columbia

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 an increasingly adopted approach for discontinuity plane identification and orientation measurement. This research proposes a computationally efficient workflow to extract planar discontinuities from a point cloud automatically. The proposed workflow includes voxel-grid downsampling as a preprocessing step to smooth the point cloud and reduce the number of points for processing. A covariance matrix is then formed, and singular value decomposition is applied to estimate normal vectors. Next, primary clustering is used to assign the normal vectors into a set of clusters. Then secondary clustering is performed to separate individual discontinuity planes in each set. Finally, plane fitting using RANSAC to extract the orientation, location, and equation of each discontinuity plane. Analysis of multiple point cloud data sets of a rock face was used to demonstrate the proposed workflow capability to extract discontinuities accurately and calculate their orientations in a computationally efficient procedure. It is demonstrated that the proposed automated procedure is superior in terms of computational run time, the accuracy of orientation results, and the efficiency for processing large point cloud data sets. Thorough comparisons with published results and analysis of performance metrics for the segmented planes are also performed. The results obtained from the proposed automated procedure for a well-studied point cloud data set show an average discontinuity orientation discrepancy of around 2° versus the ground truth results.

Text

2022-06-20

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/81760

Civil Engineering

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/