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

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Journal of Rock Mechanics and Geotechnical Engineering 2021年第4期13卷 920-936页

作者： Rushikesh Battulwar Masoud Zare-Naghadehi Ebrahim Emami Javad Sattarvand Department of Mining and Metallurgical Engineering Mackay School of Earth Sciences and EngineeringThe University of NevadaRenoNV89557USA Department of Computer Science and Engineering The University of NevadaRenoNV89557USA

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)*** provides several methodologies for acquiring discontinuity measurements from 3D models,such as point clouds generated using laser scanning or ***,even with numerous automated and semiautomated methods presented in the literature,there is not one single method that can automatically characterize discontinuities accurately in a minimum of *** 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 *** a result of this review,we identify the strengths and drawbacks of each method used for extracting those *** found that the approaches based on voxels and region growing are superior in extracting joint planes from 3D point *** tensor voting with trace growth algorithm is a robust method for measuring joint trace length from 3D *** is estimated by calculating the perpendicular distance between joint *** independent roughness indices are presented to quantify roughness from 3D surface models,but there is a need to incorporate these indices into automated *** is a lack of efficient algorithms for direct computation of block size from 3D rock mass surface models.

关键词： Rock mass discontinuity characterization Automatic extraction Three-dimensional(3D)point cloud

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