A state-of-the-art review of automated extraction of rock mass discontinuity characteristics using three-dimensional surface models

Abstract

In the last two decades, significant research has been conducted in the field of automated extraction of rock mass discontinuity characteristics from three-dimensional (3D) models. This provides several methodologies for acquiring discontinuity measurements from 3D models, such as point clouds generated using laser scanning or photogrammetry. However, even with numerous automated and semi-automated methods presented in the literature, there is not one single method that can automatically characterize discontinuities accurately in a minimum of time. In this paper, we critically review all the existing methods proposed in the literature for the extraction of discontinuity characteristics such as joint sets and orientations, persistence, joint spacing, roughness and block size using point clouds, digital elevation maps, or meshes. As a result of this review, we identify the strengths and drawbacks of each method used for extracting those characteristics. We found that the approaches based on voxels and region growing are superior in extracting joint planes from 3D point clouds. Normal tensor voting with trace growth algorithm is a robust method for measuring joint trace length from 3D meshes. Spacing is estimated by calculating the perpendicular distance between joint planes. Several independent roughness indices are presented to quantify roughness from 3D surface models, but there is a need to incorporate these indices into automated methodologies. There is a lack of efficient algorithms for direct computation of block size from 3D rock mass surface models. • Review of automatic and semi-automatic methods for discontinuity characterization. • Focused on joint sets, orientation, persistence, spacing, roughness and block size. • Strengths and limitations of methods are discussed from a practical perspective. • Computation time, number of parameters, accuracy, and robustness are discussed. • Region growing was found fast and accurate for joint detection from LiDAR data.