Automatic identification of discontinuities and refined modeling of rock blocks from 3D point cloud data of rock surfaces
The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment. Traditional manual measurement is often dangerous or unreachable at some high-steep rock slopes. In contrast, unmanned aerial vehicle (UAV) ph...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
|
| Series: | Journal of Rock Mechanics and Geotechnical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674775524005109 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849321745586061312 |
|---|---|
| author | Yaopeng Ji Shengyuan Song Jianping Chen Jingyu Xue Jianhua Yan Yansong Zhang Di Sun Qing Wang |
| author_facet | Yaopeng Ji Shengyuan Song Jianping Chen Jingyu Xue Jianhua Yan Yansong Zhang Di Sun Qing Wang |
| author_sort | Yaopeng Ji |
| collection | DOAJ |
| description | The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment. Traditional manual measurement is often dangerous or unreachable at some high-steep rock slopes. In contrast, unmanned aerial vehicle (UAV) photogrammetry is not limited by terrain conditions, and can efficiently collect high-precision three-dimensional (3D) point clouds of rock masses through all-round and multiangle photography for rock mass characterization. In this paper, a new method based on a 3D point cloud is proposed for discontinuity identification and refined rock block modeling. The method is based on four steps: (1) Establish a point cloud spatial topology, and calculate the point cloud normal vector and average point spacing based on several machine learning algorithms; (2) Extract discontinuities using the density-based spatial clustering of applications with noise (DBSCAN) algorithm and fit the discontinuity plane by combining principal component analysis (PCA) with the natural breaks (NB) method; (3) Propose a method of inserting points in the line segment to generate an embedded discontinuity point cloud; and (4) Adopt a Poisson reconstruction method for refined rock block modeling. The proposed method was applied to an outcrop of an ultrahigh steep rock slope and compared with the results of previous studies and manual surveys. The results show that the method can eliminate the influence of discontinuity undulations on the orientation measurement and describe the local concave-convex characteristics on the modeling of rock blocks. The calculation results are accurate and reliable, which can meet the practical requirements of engineering. |
| format | Article |
| id | doaj-art-a6b42e07206941eeb69efaa1a10a75b1 |
| institution | Kabale University |
| issn | 1674-7755 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Rock Mechanics and Geotechnical Engineering |
| spelling | doaj-art-a6b42e07206941eeb69efaa1a10a75b12025-08-20T03:49:41ZengElsevierJournal of Rock Mechanics and Geotechnical Engineering1674-77552025-05-011753093310610.1016/j.jrmge.2024.04.039Automatic identification of discontinuities and refined modeling of rock blocks from 3D point cloud data of rock surfacesYaopeng Ji0Shengyuan Song1Jianping Chen2Jingyu Xue3Jianhua Yan4Yansong Zhang5Di Sun6Qing Wang7College of Construction Engineering, Jilin University, Changchun, 130026, ChinaCollege of Construction Engineering, Jilin University, Changchun, 130026, ChinaCollege of Construction Engineering, Jilin University, Changchun, 130026, ChinaCollege of Construction Engineering, Jilin University, Changchun, 130026, ChinaSchool of Earth Sciences and Engineering, Hohai University, Nanjing, 210098, ChinaCollege of Construction Engineering, Jilin University, Changchun, 130026, ChinaCollege of Construction Engineering, Jilin University, Changchun, 130026, ChinaCollege of Construction Engineering, Jilin University, Changchun, 130026, China; Corresponding author.The spatial distribution of discontinuities and the size of rock blocks are the key indicators for rock mass quality evaluation and rockfall risk assessment. Traditional manual measurement is often dangerous or unreachable at some high-steep rock slopes. In contrast, unmanned aerial vehicle (UAV) photogrammetry is not limited by terrain conditions, and can efficiently collect high-precision three-dimensional (3D) point clouds of rock masses through all-round and multiangle photography for rock mass characterization. In this paper, a new method based on a 3D point cloud is proposed for discontinuity identification and refined rock block modeling. The method is based on four steps: (1) Establish a point cloud spatial topology, and calculate the point cloud normal vector and average point spacing based on several machine learning algorithms; (2) Extract discontinuities using the density-based spatial clustering of applications with noise (DBSCAN) algorithm and fit the discontinuity plane by combining principal component analysis (PCA) with the natural breaks (NB) method; (3) Propose a method of inserting points in the line segment to generate an embedded discontinuity point cloud; and (4) Adopt a Poisson reconstruction method for refined rock block modeling. The proposed method was applied to an outcrop of an ultrahigh steep rock slope and compared with the results of previous studies and manual surveys. The results show that the method can eliminate the influence of discontinuity undulations on the orientation measurement and describe the local concave-convex characteristics on the modeling of rock blocks. The calculation results are accurate and reliable, which can meet the practical requirements of engineering.http://www.sciencedirect.com/science/article/pii/S1674775524005109Three-dimensional (3D) point cloudRock massAutomatic identificationRefined modelingUnmanned aerial vehicle (UAV) |
| spellingShingle | Yaopeng Ji Shengyuan Song Jianping Chen Jingyu Xue Jianhua Yan Yansong Zhang Di Sun Qing Wang Automatic identification of discontinuities and refined modeling of rock blocks from 3D point cloud data of rock surfaces Journal of Rock Mechanics and Geotechnical Engineering Three-dimensional (3D) point cloud Rock mass Automatic identification Refined modeling Unmanned aerial vehicle (UAV) |
| title | Automatic identification of discontinuities and refined modeling of rock blocks from 3D point cloud data of rock surfaces |
| title_full | Automatic identification of discontinuities and refined modeling of rock blocks from 3D point cloud data of rock surfaces |
| title_fullStr | Automatic identification of discontinuities and refined modeling of rock blocks from 3D point cloud data of rock surfaces |
| title_full_unstemmed | Automatic identification of discontinuities and refined modeling of rock blocks from 3D point cloud data of rock surfaces |
| title_short | Automatic identification of discontinuities and refined modeling of rock blocks from 3D point cloud data of rock surfaces |
| title_sort | automatic identification of discontinuities and refined modeling of rock blocks from 3d point cloud data of rock surfaces |
| topic | Three-dimensional (3D) point cloud Rock mass Automatic identification Refined modeling Unmanned aerial vehicle (UAV) |
| url | http://www.sciencedirect.com/science/article/pii/S1674775524005109 |
| work_keys_str_mv | AT yaopengji automaticidentificationofdiscontinuitiesandrefinedmodelingofrockblocksfrom3dpointclouddataofrocksurfaces AT shengyuansong automaticidentificationofdiscontinuitiesandrefinedmodelingofrockblocksfrom3dpointclouddataofrocksurfaces AT jianpingchen automaticidentificationofdiscontinuitiesandrefinedmodelingofrockblocksfrom3dpointclouddataofrocksurfaces AT jingyuxue automaticidentificationofdiscontinuitiesandrefinedmodelingofrockblocksfrom3dpointclouddataofrocksurfaces AT jianhuayan automaticidentificationofdiscontinuitiesandrefinedmodelingofrockblocksfrom3dpointclouddataofrocksurfaces AT yansongzhang automaticidentificationofdiscontinuitiesandrefinedmodelingofrockblocksfrom3dpointclouddataofrocksurfaces AT disun automaticidentificationofdiscontinuitiesandrefinedmodelingofrockblocksfrom3dpointclouddataofrocksurfaces AT qingwang automaticidentificationofdiscontinuitiesandrefinedmodelingofrockblocksfrom3dpointclouddataofrocksurfaces |