94% of researchers rate our articles as excellent or good
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Find out more
ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 13 - 2025 | doi: 10.3389/feart.2025.1550986
This article is part of the Research Topic Physical Properties and Mechanical Theory of Rock Materials with Defects View all 10 articles
Discontinuous surface extraction method based on 3D point cloud
Provisionally accepted- 1 Zhongjiao Hehai Engineering Co., Ltd., Taixing, China
- 2 Dalian University of Technology, Dalian, Liaoning Province, China
- 3 University of Science and Technology Liaoning, Anshan, China
In underground engineering, rock mass failure due to shearing and tensile loads is common. To investigate the morphological characteristics of sandstone cross-sections under various loading methods, point cloud data of sandstone cross-sections were obtained using a three-dimensional surface topography instrument.An automatic recognition method for discontinuous surfaces based on point cloud data is proposed. This method integrates the K-nearest neighbor (KNN) search and the random sample consensus (RANSAC) algorithm to calculate the normal vectors. It employs mean shift clustering for preliminary grouping and identifies discontinuities through Euclidean clustering. The dip angle, trend, and area of the dominant discontinuous surfaces were extracted and quantified. Additionally, through the analytical application of two examples, hexahedra and on-site rock joints, the comparative results with other algorithms indicate that the proposed method exhibits high consistency in dip angle and trend extraction with minimal error. Particularly in complex rock formations, this method effectively identifies small-scale discontinuities and accurately calculates their areas, thereby demonstrating its reliability and robustness in practical applications.
Keywords: Rock fracture, 3D point cloud, clustering, Discontinuity orientation, Automatic Extraction
Received: 24 Dec 2024; Accepted: 24 Feb 2025.
Copyright: © 2025 Zhu, Li, Li, Sun and Ren. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Fuqiang Ren, University of Science and Technology Liaoning, Anshan, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.