Discontinuity spacing analysis in rock masses using 3D point clouds

Riquelme, Adrián; Abellán, Antonio; Tomás, Roberto

doi:10.1016/j.enggeo.2015.06.009

Cited by 121 publications

(53 citation statements)

References 25 publications

(28 reference statements)

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“…The Kernel Density Estimation (KDE) was used to find the main directions and the density-based scanning principle was used to identify the clusters. The authors published the Discontinuity Set Extractor (DSE) open-source software and this software also calculated the normal spacing and the discontinuity persistence [40,41]. By considering the Hough Transform and region growing simultaneously, Leng et al [42] proposed a multi-scale surface-detection algorithm.…”

Section: Other Methodsmentioning

confidence: 99%

Major Orientation Estimation-Based Rock Surface Extraction for 3D Rock-Mass Point Clouds

2019

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In the fields of 3D modeling, analysis of discontinuities and engineering calculation, surface extraction is of great importance. The rapid development of photogrammetry and Light Detection and Ranging (LiDAR) technology facilitates the study of surface extraction. Automatic extraction of rock surfaces from 3D rock-mass point clouds also becomes the basis of 3D modeling and engineering calculation of rock mass. This paper presents an automated and effective method for extracting rock surfaces from unorganized rock-mass point clouds. This method consists of three stages: (i) clustering based on voxels; (ii) estimating major orientations based on Gaussian Kernel and (iii) rock surface extraction. Firstly, the two-level spatial grid is used for fast voxelization and segmenting the point cloud into three types of voxels, including coplanar, non-coplanar and sparse voxels. Secondly, the coplanar voxels, rather than the scattered points, are employed to estimate major orientations by using a bivariate Gaussian Kernel. Finally, the seed voxels are selected on the basis of major orientations and the region growing method based on voxels is applied to extract rock surfaces, resulting in sets of surface clusters. The sub-surfaces of each cluster are coplanar or parallel. In this paper, artificial icosahedron point cloud and natural rock-mass point clouds are used for testing the proposed method, respectively. The experimental results show that, the proposed method can effectively and accurately extract rock surfaces in unorganized rock-mass point clouds.

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Section: Other Methodsmentioning

confidence: 99%

Major Orientation Estimation-Based Rock Surface Extraction for 3D Rock-Mass Point Clouds

2019

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“…Computation of discontinuity spacings from 3D point clouds has rapidly evolved during the most recent decade: Slob (2010) considered discontinuities as persistent and measured the spacing with a virtual scanline, and Riquelme et al (2015) considered both persistence and impersistence, assuming that the planes of a discontinuity set are parallel and proposed a method to measure the normal spacing for persistent and non-persistent discontinuities with 3D datasets, enabling the study and discussion on how to extract persistence information from 3D datasets.…”

Section: Figurementioning

confidence: 99%

Automatic Mapping of Discontinuity Persistence on Rock Masses Using 3D Point Clouds

et al. 2018

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Finding new ways to quantify discontinuity persistence values in rock masses in an automatic or semi-automatic manner is a considerable challenge, as an alternative to the use of traditional methods based on measuring patches or traces with tapes. Remote sensing techniques potentially provide new ways of analysing visible data from the rock mass. This work presents a methodology for the automatic mapping of discontinuity persistence on rock masses, using 3D point clouds. The method proposed herein starts by clustering points that belong to patches of a given discontinuity. Coplanar clusters are then merged into a single group of points. Persistence is measured in the directions of the dip and strike for each coplanar set of points, resulting in the extraction of the length of the maximum chord and the area of the convex hull. The proposed approach is implemented in a graphic interface with open source software. Three case studies are utilized to illustrate the methodology: (1) small-scale laboratory setup consisting of a regular distribution of cubes with similar dimensions, (2) more complex geometry consisting of a real rock mass surface in an excavated cavern and (3) slope with persistent sub-vertical discontinuities. Results presented good agreement with field measurements, validating the methodology. Complexities and difficulties related to the method (e.g,. natural discontinuity waviness) are reported and discussed. An assessment on the applicability of the method to the 3D point cloud is also presented. Utilization of remote sensing data for a more objective characterization of the persistence of planar discontinuities affecting rock masses is highlighted herein. Dip angle of a discontinuity set Mean trace termination or persistence frequency Mean of point-plane distances Standard deviation of the distances point-plane distances

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“…A photogrammetric method for earth scientists to obtain accurate measurements while minimizing the extra bulk and weight of the equipment was developed by Rieke-Zapp et al [8]. The complete characterization of rock masses using advanced remote sensing technologies like LiDAR to detect the rock mass together with discontinuities was highlighted by Riquelme et al [9]. Fais et al utilized the SfM photogrammetry and TLS as non-invasive techniques to characterize various rock samples [10].…”

Section: Introductionmentioning

confidence: 99%

TLS and SfM Approach for Bulk Density Determination of Excavated Heterogeneous Raw Materials

et al. 2020

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A frequently recurring problem in the extraction of mineral resources (especially heterogeneous mineral resources) is the rapid operative determination of the extracted quantity of raw material in a surface quarry. This paper deals with testing and analyzing the possibility of using unconventional methods such as digital close-range photogrammetry and terrestrial laser scanning in the process of determining the bulk density of raw material under in situ conditions. A model example of a heterogeneous deposit is the perlite deposit Lehôtka pod Brehmi (Slovakia). Classical laboratory methods for determining bulk density were used to verify the results of the in situ method of bulk density determination. Two large-scale samples (probes) with an approximate volume of 7 m 3 and 9 m 3 were realized in situ. 6 point samples (LITH) were taken for laboratory determination. By terrestrial laser scanning (TLS) measurement from 2 scanning stations, point clouds with approximately 163,000/143,000 points were obtained for each probe. For Structure-from-Motion (SfM) photogrammetry, 49/55 images were acquired for both probes, with final point clouds containing approximately 155,000/141,000 points. Subsequently, the bulk densities of the bulk samples were determined by the calculation from in situ measurements by TLS and SfM photogrammetry. Comparison of results of the field in situ measurements (1841 kg·m −3 ) and laboratory measurements (1756 kg·m −3 ) showed only a 4.5% difference in results between the two methods for determining the density of heterogeneous raw materials, confirming the accuracy of the used in situ methods. For the determination of the loosening coefficient, the material from both large-scale samples was transferred on a horizontal surface. Their volumes were determined by TLS. The loosening coefficient for the raw material of 1.38 was calculated from the resulting values.

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Discontinuity spacing analysis in rock masses using 3D point clouds

Cited by 121 publications

References 25 publications

Major Orientation Estimation-Based Rock Surface Extraction for 3D Rock-Mass Point Clouds

Major Orientation Estimation-Based Rock Surface Extraction for 3D Rock-Mass Point Clouds

Automatic Mapping of Discontinuity Persistence on Rock Masses Using 3D Point Clouds

TLS and SfM Approach for Bulk Density Determination of Excavated Heterogeneous Raw Materials

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