Semi‐automatic identification of discontinuity parameters in rock masses based on Unmanned Aerial Vehicle photography

Abstract

Efficient and accurate extraction of discontinuity geometric parameters is significant for evaluating the stability and seepage characteristics of a rock mass. Traditional means of rock discontinuity measurement are susceptible to the influence of terrain and have issues such as high operational workload and low efficiency. An approach was proposed to comprehensively and efficiently obtain the geometric parameter information of the discontinuity, using an Unmanned Aerial Vehicle (UAV), which consists of the following five procedures: (1) measurement of a rock slope with UAV, (2) creation of three‐dimensional (3D) point cloud model for the slope, (3) voxel filtering of point cloud data, (4) planar segmentation based on modified region growing (MRG) algorithm, and (5) acquisition of parameter set of the discontinuity (orientation, trace length, and spacing). A new code, FacetDetect, has been developed based on this method to identify discontinuity from a 3D point cloud. Meanwhile, the trace length and spacing are calculated by the code written in Python. A comparison with the three‐point method reveals that most errors are less than 3° for dip angles and dip directions. These deviations are reasonable and confirm the reliability of the method. Overall, this method is a valuable reference for automatic discontinuity interpretation and related fields.