Automatic extraction of discontinuity orientation from rock mass surface 3D point cloud

Chen, Jianqin; Zhu, Hehua; Li, Xiao Jun

doi:10.1016/j.cageo.2016.06.015

Cited by 112 publications

(35 citation statements)

References 21 publications

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“…Illustrated by the case of the typical rock slope, we clarified in detail how to identify the main potential directions of DPCA, determine the number of discontinuity sets by the silhouette coefficient (Section 2.3), segment the discontinuity sets by HDBSCAN (Section 2.4), fit the discontinuities by the RANSAC method (Section 2.5), and compare the extracted results with those obtained by Riquelme et al [40] and Chen et al [37] (Section 2.6). The results show that (1) the new method has higher extraction precision.…”

Section: Comparison Of the Extracting Results Of The Rock Slopementioning

confidence: 99%

“…First, remove noise points and outliers from the point clouds of the rock mass, then resample the point clouds and use the Delaunay algorithm to generate a TIN of the rock mass surface. Compared to the regular grid model, TIN has the advantages of reducing data redundancy, better performance of variation characteristics, and easy calculation [9,37]. (2) Discontinuity set recognition.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, the Delaunay algorithm was used to create a TIN from the point clouds. The preprocessing steps in this paper reference the method proposed by Chen et al [37]. Figure 3a shows the original point clouds of the rock slope.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…The second method is to extract them from the digital surface model (DSM) of the rock mass. The steps are as follows: (a) firstly, use the triangulated irregular network (TIN) for the 3D point clouds in order to create the DSM of rock mass, calculate the normal vector of each triangle, and determine the same discontinuity set by the triangles with similar normal vectors; (b) then use the region growing algorithm to grow the triangles of the same discontinuity set and identify a single discontinuity in each discontinuity set; and finally (c) calculate the orientation of each discontinuity [9,37]. However, in the first method, the radius (or numbers) of the neighboring points is related to the structure and surface roughness of the rock mass.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A New Method for Automatic Extraction and Analysis of Discontinuities Based on TIN on Rock Mass Surfaces

Wang

et al. 2021

Remote Sensing

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Light detection and ranging (LiDAR) can quickly and accurately obtain 3D point clouds on the surface of rock masses, and on the basis of this, discontinuity information can be extracted automatically. This paper proposes a new method to automatically extract discontinuity information from 3D point clouds on the surface of rock masses. This method first applies the improved K-means algorithm based on the clustering algorithm by fast search and find of density peaks (DPCA) and the silhouette coefficient in the cluster validity index to identify the discontinuity sets of rock masses, and then uses the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm to segment the discontinuity sets and to extract each discontinuity from a discontinuity set. Finally, the random sampling consistency (RANSAC) method is used to fit the discontinuities and to calculate their parameters. The 3D point clouds of the typical rock slope in the Rockbench repository is used to extract the discontinuity orientations using the new method, and these are compared with the results obtained from the classical approach and the previous automatic methods. The results show that, compared to the results obtained by Riquelme et al. in 2014, the average deviation of the dip direction and dip angle is reduced by 26% and 8%, respectively; compared to the results obtained by Chen et al. in 2016, the average deviation of the dip direction and dip angle is reduced by 39% and 40%, respectively. The method is also applied to an artificial quarry slope, and the average deviation of the dip direction and dip angle is 5.3° and 4.8°, respectively, as compared to the manual method. Furthermore, the related parameters are analyzed. The study shows that the new method is reliable, has a higher precision when identifying rock mass discontinuities, and can be applied to practical engineering.

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Section: Comparison Of the Extracting Results Of The Rock Slopementioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Method for Automatic Extraction and Analysis of Discontinuities Based on TIN on Rock Mass Surfaces

Wang

et al. 2021

Remote Sensing

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show abstract

“…Reference [10] researched the method for rock surface extraction from 3D rock-mass point clouds based on major orientation estimation. The research in [11] developed a semi-automatic method for rock mass joint recognition from 3D point clouds while the study in [12] further explored the automatic extraction of discontinuity orientation. Reference [13] performed geomechanical analysis on high-resolution 3D point clouds collected by TLS for slope stability condition assessment.…”

Section: Introductionmentioning

confidence: 99%

Augmented Reality Mapping of Rock Mass Discontinuities and Rockfall Susceptibility Based on Unmanned Aerial Vehicle Photogrammetry

et al. 2019

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In rockfall hazard management, the investigation and detection of potential rockfall source areas on rock cliffs by remote-sensing-based susceptibility analysis are of primary importance. However, when the rockfall analysis results are used as feedback to the fieldwork, the irregular slope surface morphology makes it difficult to objectively locate the risk zones of hazard maps on the real slopes, and the problem of straightforward on-site visualization of rockfall susceptibility remains a research gap. This paper presents some of the pioneering studies on the augmented reality (AR) mapping of geospatial information from cyberspace within 2D screens to the physical world for on-site visualization, which directly recognizes the rock mass and superimposes corresponding rock discontinuities and rockfall susceptibility onto the real slopes. A novel method of edge-based tracking of the rock mass target for mobile AR is proposed, where the model edges extracted from unmanned aerial vehicle (UAV) structure-from-motion (SfM) 3D reconstructions are aligned with the corresponding actual rock mass to estimate the camera pose accurately. Specifically, the visually prominent edges of dominant structural planes were first explored and discovered to be a robust visual feature of rock mass for AR tracking. The novel approaches of visual-geometric synthetic image (VGSI) and prominent structural plane (Pro-SP) were developed to extract structural planes with identified prominent edges as 3D template models which could provide a pose estimation reference. An experiment verified that the proposed Pro-SP template model could effectively improve the edge tracking performance and quality, and this approach was relatively robust to the changes of sunlight conditions. A case study was carried out on a typical roadcut cliff in the Mentougou District of Beijing, China. The results validate the scalability of the proposed mobile AR strategy, which is applicable and suitable for cliff-scale fieldwork. The results also demonstrate the feasibility, efficiency, and significance of the geoinformation AR mapping methodology for on-site zoning and locating of potential rockfalls, and providing relevant guidance for subsequent detailed site investigation.

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Beishan exploration tunnel surrounding rock discontinuity identification based on structure from motion photogrammetry technology

Xuan,

Zhang,

et al. 2024

Engineering Reports

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The Beishan Exploration Tunnel (BET) is a facility built to develop technologies associated with the safety of China's first high‐level radioactive nuclear waste(HLW) disposal. The surrounding rock discontinuity identification is a key research topic in BET, which could provide essential geological data for future HLW disposal stability and integrity research. This article presents the rock discontinuity identification research progress in BET based on Structure from Motion (SfM) photogrammetry technology. The discontinuity identification algorithm is improved by introducing the region‐growing algorithm to optimize the candidate subplane. This algorithm automatically picks the seed, avoids human intervention, and thus increases the work efficiency of the discontinuity identification. The FCM method is improved by embedding with the CFSFDP algorithm in the discontinuity sets grouping. The CFSFDP algorithm coincides well with the Fisher distribution of discontinuity orientations, which is suitable for the Beishan situation. A parallel scheme is used when implementing the method, which accelerates the discontinuity calculation. This improved rock discontinuity identification method was tested on a slope above the BET and applied in the BET. The discontinuity identification results were compared with the results from the manual field measurement and the open‐source software DSE. The results show that the improved discontinuity identification method obtains reliable discontinuity data and costs less time and human workload than the other two methods. The surrounding rock discontinuity identification research provides a powerful tool for the Beishan HLW disposal geological investigation.

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Automatic extraction of discontinuity orientation from rock mass surface 3D point cloud

Cited by 112 publications

References 21 publications

A New Method for Automatic Extraction and Analysis of Discontinuities Based on TIN on Rock Mass Surfaces

A New Method for Automatic Extraction and Analysis of Discontinuities Based on TIN on Rock Mass Surfaces

Augmented Reality Mapping of Rock Mass Discontinuities and Rockfall Susceptibility Based on Unmanned Aerial Vehicle Photogrammetry

Beishan exploration tunnel surrounding rock discontinuity identification based on structure from motion photogrammetry technology

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