scholarly journals A new approach for semi-automatic rock mass joints recognition from 3D point clouds

2014 ◽  
Vol 68 ◽  
pp. 38-52 ◽  
Author(s):  
Adrián J. Riquelme ◽  
A. Abellán ◽  
R. Tomás ◽  
M. Jaboyedoff
Keyword(s):  
Rock Mass ◽  
Point Clouds ◽  
New Approach ◽  
3D Point Clouds

scholarly journals Surface Subsidence Prognosis above an Underground Longwall Excavation and Based on 3D Point Cloud Analysis

Minerals ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 82 ◽  
Author(s):  
Andrej Pal ◽  
Janez Rošer ◽  
Milivoj Vulić
Keyword(s):  
Rock Mass ◽  
Longwall Mining ◽  
Underground Mining ◽  
Point Clouds ◽  
Surface Subsidence ◽  
Protection Measures ◽  
Engineering Research ◽  
3D Point Clouds ◽  
Research Activities ◽  
And Control

Impacts of underground mining have been reduced by continuous environmental endeavors, scientific, and engineering research activities, whose main object is the behavior and control of the undermined rock mass and the subsequent surface subsidence. In the presented Velenje case of underground sublevel longwall mining where coal is being exploited both horizontal and vertical, backfilling processes and accompanying fracturing in the coal layer, and rock mass are causing uncontrolled subsidence of the surface above. 3D point clouds of the study were acquired in ten epochs and at excavation heights on the front were measured at the same epochs. By establishing a sectors layout in the observational area, smaller point clouds were obtained, to which planes were fitted and centroids of these planes then calculated. Centroid heights were analyzed with the FNSE model to estimate the time of consolidation and modified according to excavation parameters to determine total subsidence after a certain period. Proposed prognosis approaches for estimating consolidation of active subsidence and long term surface environmental protection measures have been proposed and presented. The C2C analysis of distances between acquired 3D point clouds was used for identification of surface subsidence, reclamation areas and sink holes, and for validation of feasibility and effectiveness of the proposed prognosis.

Automatic characterization of rock mass discontinuities using 3D point clouds

2019 ◽  
Vol 259 ◽  
pp. 105131 ◽  
Author(s):  
Xiaojun Li ◽  
Ziyang Chen ◽  
Jianqin Chen ◽  
Hehua Zhu
Keyword(s):  
Rock Mass ◽  
Point Clouds ◽  
3D Point Clouds

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

2021 ◽  
Vol 13 (15) ◽  
pp. 2894
Author(s):  
Xiang Wu ◽  
Fengyan Wang ◽  
Mingchang Wang ◽  
Xuqing Zhang ◽  
Qing Wang ◽  
...  
Keyword(s):  
Rock Mass ◽  
Clustering Algorithm ◽  
Point Clouds ◽  
New Method ◽  
Rock Masses ◽  
Average Deviation ◽  
Density Peaks ◽  
3D Point Clouds ◽  
Discontinuity Sets ◽  
Dip Angle

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.

scholarly journals An Effective Approach for Rock Mass Discontinuity Extraction Based on Terrestrial LiDAR Scanning 3D Point Clouds

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 26734-26742 ◽  
Author(s):  
Xianquan Han ◽  
Shengmei Yang ◽  
Fangfang Zhou ◽  
Jian Wang ◽  
Dongbo Zhou
Keyword(s):  
Rock Mass ◽  
Point Clouds ◽  
Terrestrial Lidar ◽  
3D Point Clouds

scholarly journals Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3926 ◽  
Author(s):  
Jongwon Kim ◽  
Jeongho Cho
Keyword(s):  
Delaunay Triangulation ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Synthetic Data ◽  
Point Clouds ◽  
Spatial Data Mining ◽  
New Approach ◽  
Clustering Technique ◽  
3D Point Clouds ◽  
Multiple Clusters

In spatial data with complexity, different clusters can be very contiguous, and the density of each cluster can be arbitrary and uneven. In addition, background noise that does not belong to any clusters in the data, or chain noise that connects multiple clusters may be included. This makes it difficult to separate clusters in contact with adjacent clusters, so a new approach is required to solve the nonlinear shape, irregular density, and touching problems of adjacent clusters that are common in complex spatial data clustering, as well as to improve robustness against various types of noise in spatial clusters. Accordingly, we proposed an efficient graph-based spatial clustering technique that employs Delaunay triangulation and the mechanism of DBSCAN (density-based spatial clustering of applications with noise). In the performance evaluation using simulated synthetic data as well as real 3D point clouds, the proposed method maintained better clustering and separability of neighboring clusters compared to other clustering techniques, and is expected to be of practical use in the field of spatial data mining.

Towards semi-automatic rock mass discontinuity orientation and set analysis from 3D point clouds

2017 ◽  
Vol 103 ◽  
pp. 164-172 ◽  
Author(s):  
Jiateng Guo ◽  
Shanjun Liu ◽  
Peina Zhang ◽  
Lixin Wu ◽  
Wenhui Zhou ◽  
...  
Keyword(s):  
Rock Mass ◽  
Point Clouds ◽  
3D Point Clouds ◽  
Discontinuity Orientation

Automatic Global Registration of 3D Point Clouds for Reverse Engineering and Inspection Processes

2011 ◽  
Vol 110-116 ◽  
pp. 4907-4913
Author(s):  
Mariano Imbert ◽  
Xiao Xing Li
Keyword(s):  
Reverse Engineering ◽  
Point Clouds ◽  
Optimization Method ◽  
Global Coordinate System ◽  
Geometric Information ◽  
Data Set ◽  
Computing Power ◽  
New Approach ◽  
Global Registration ◽  
3D Point Clouds

Registration of 3D point clouds is one of the most fundamental phases during the process of reverse engineering and most challenging at the same time. This phase consists on matching two or more different point clouds into one data set in order to have them share the same global coordinate system. In this paper we present a new approach for automatic registration of 3D point clouds that uses the genetic algorithm (GA) as a global optimization method. We introduce a trips extraction technique for rough registration, which extracts important geometric information from a point cloud. Another contribution in this paper is the introduction of the Interpenetration Fraction Measure (IFM), which maximizes the number of points that overlap two different point clouds. The algorithm we present also takes advantage of the parallel computing power of today’s multi-core processors, and other techniques for further efficiency. Finally, we present some experimental data with comparisons for analysis and further discussion about the algorithm’s performance.

scholarly journals SEMI-AUTOMATIC ROCK MASS GEOMETRY ANALYSIS FROM A DENSE 3D POINT CLOUD WITH DISCONTINUITYLAB

2020 ◽  
Vol V-2-2020 ◽  
pp. 679-686
Author(s):  
P. Caudal ◽  
E. Simonetto ◽  
V. Merrien-Soukatchoff ◽  
T. J. B. Dewez
Keyword(s):  
Rock Mass ◽  
Point Cloud ◽  
Point Clouds ◽  
Underground Mines ◽  
Open Pit ◽  
3D Point Cloud ◽  
Additional Information ◽  
3D Point Clouds ◽  
Geometry Analysis ◽  
2D And 3D

Abstract. 2D and 3D imageries can allow the optimization of rock mass exploitation (quarries, roads, rail networks, open pit, potentially tunnels and underground mines networks). The increasingly common use of photogrammetry makes it possible to obtain georeferenced 3D point clouds that are useful for understanding the rock mass. Indeed, new structural analysis solutions have been proposed since the advent of the 3D technologies. These methods are essentially focused on the production of digital stereonet. Production of additional information from 3D point clouds are possible to better define the structure of the rock mass, in particular the quantification of the discontinuities density. The aim of this paper is to test and validate a new method that provides statistics on the distances between the discontinuity planes. This solution is based on exploiting the information previously extracted from the segmentation of the discontinuity planes of a point cloud and their classification in family. In this article, the proposed solution is applied on two multiscale examples, firstly to validate it with a virtual synthetic outcrop and secondly to test it on a real outcrop. To facilitate these analyses, a software called DiscontinuityLab has been developed and used for the treatments.

scholarly journals Metrics of Growth Habit Derived from the 3D Tree Point Cloud Used for Species Determination—A New Approach in Botanical Taxonomy Tested on Dragon Tree Group Example

Forests ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 272 ◽  
Author(s):  
Petr Vahalík ◽  
Karel Drápela ◽  
Andrea Procházková ◽  
Zdeněk Patočka ◽  
Marie Balková ◽  
...  
Keyword(s):  
Growth Habit ◽  
Three Dimensional ◽  
Point Clouds ◽  
Species Determination ◽  
New Approach ◽  
Three Dimensional Modeling ◽  
3D Point Clouds ◽  
Dimensional Modeling ◽  
Morphological Differences ◽  
Individual Trees

Detailed, three-dimensional modeling of trees is a new approach in botanical taxonomy. Representations of individual trees are a prerequisite for accurate assessments of tree growth and morphological metronomy. This study tests the abilities of 3D modeling of trees to determine the various metrics of growth habit and compare morphological differences. The study included four species of the genus Dracaena: D. draco, D. cinnabari, D. ombet, and D. serrulata. Forty-nine 3D tree point clouds were created, and their morphological metrics were derived and compared. Our results indicate the possible application of 3D tree point clouds to dendrological taxonomy. Basic metrics of growth habit and coefficients derived from the 3D point clouds developed in the present study enable the statistical evaluation of differences among dragon tree species.

Sign in / Sign up

Export Citation Format

Share Document