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

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.

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Supervised Detection of Façade Openings in 3D Point Clouds with Thermal Attributes

Remote Sensing ◽

10.3390/rs12030543 ◽

2020 ◽

Vol 12 (3) ◽

pp. 543 ◽

Cited By ~ 1

Author(s):

Małgorzata Jarząbek-Rychard ◽

Dong Lin ◽

Hans-Gerd Maas

Keyword(s):

Large Scale ◽

Conditional Random Field ◽

Contextual Information ◽

Point Clouds ◽

Thermal Infrared ◽

Classification Performance ◽

Space Images ◽

3D Point Clouds ◽

3D Scene ◽

And Control

Targeted energy management and control is becoming an increasing concern in the building sector. Automatic analyses of thermal data, which minimize the subjectivity of the assessment and allow for large-scale inspections, are therefore of high interest. In this study, we propose an approach for a supervised extraction of façade openings (windows and doors) from photogrammetric 3D point clouds attributed to RGB and thermal infrared (TIR) information. The novelty of the proposed approach is in the combination of thermal information with other available characteristics of data for a classification performed directly in 3D space. Images acquired in visible and thermal infrared spectra serve as input data for the camera pose estimation and the reconstruction of 3D scene geometry. To investigate the relevance of different information types to the classification performance, a Random Forest algorithm is applied to various sets of computed features. The best feature combination is then used as an input for a Conditional Random Field that enables us to incorporate contextual information and consider the interaction between the points. The evaluation executed on a per-point level shows that the fusion of all available information types together with context consideration allows us to extract objects with 90% completeness and 95% correctness. A respective assessment executed on a per-object level shows 97% completeness and 88% accuracy.

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Automatic characterization of rock mass discontinuities using 3D point clouds

Engineering Geology ◽

10.1016/j.enggeo.2019.05.008 ◽

2019 ◽

Vol 259 ◽

pp. 105131 ◽

Cited By ~ 6

Author(s):

Xiaojun Li ◽

Ziyang Chen ◽

Jianqin Chen ◽

Hehua Zhu

Keyword(s):

Rock Mass ◽

Point Clouds ◽

3D Point Clouds

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A Comprehensive Method for Subsidence Prediction on Two-Seam Longwall Mining

Energies ◽

10.3390/en12163139 ◽

2019 ◽

Vol 12 (16) ◽

pp. 3139 ◽

Cited By ~ 3

Author(s):

Bin Zhang ◽

Jiacheng Ye ◽

Zhongjian Zhang ◽

Liang Xu ◽

Nengxiong Xu

Keyword(s):

Longwall Mining ◽

Underground Mining ◽

Surface Subsidence ◽

Orthogonal Experimental Design ◽

Parameter Inversion ◽

Subsidence Prediction ◽

Reliable Assessment ◽

Seam Mining ◽

Subsidence Monitoring ◽

Overlying Strata

The purpose of mining subsidence prediction is to establish a reliable assessment for surface subsidence resulting from underground mining. In this study, a new method for predicting subsidence in two-seam mining is proposed. First, the surface subsidence due to mining the upper seam is monitored. Then, taking the subsidence data as indicators, the optimal mechanical parameters of overlying strata can be obtained by orthogonal experimental design and inverse analysis of numerical simulation. Finally, further subsidence is calculated and predicted by the numerical model. A case of two-seam underground mining is studied using this methodology. This coal mine is located in the Dongsheng coal field in Inner Mongolia, China. Based on GPS surface subsidence monitoring and parameter inversion, the subsidence induced by two-seam mining is estimated and predicted. This study shows that the ratio of the height of overlying strata to mining thickness (H/M), mining configuration and adjacent mining have a significant effect on the surface subsidence caused by two-seam mining. By parameter inversion, the proposed optimal parameters can be applied to predict the subsidence of a nearby mine with similar stratigraphic conditions. Furthermore, this methodology can also be used to predict the subsidence caused by mining of more than two seams.

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Using open-source software for extracting geomechanical parameters of a rock mass from 3D point clouds: Discontinuity set extractor and SMRTool

Rock Mechanics and Rock Engineering: From the Past to the Future ◽

10.1201/9781315388502-190 ◽

2016 ◽

Cited By ~ 3

Author(s):

A Riquelme ◽

R Tomás ◽

M Cano ◽

A Abellán

Keyword(s):

Rock Mass ◽

Open Source ◽

Open Source Software ◽

Point Clouds ◽

3D Point Clouds ◽

Geomechanical Parameters

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Ground surface subsidence as effect of underground mining of the thick coal seams in the Jiu valley basin / Osiadanie gruntów wskutek wybierania pokładów o dużej miąższości w kopalniach zagłębia w dolinie Jiu

Archives of Mining Sciences ◽

10.2478/v10267-012-0036-z ◽

2012 ◽

Vol 57 (3) ◽

pp. 547-577 ◽

Cited By ~ 2

Author(s):

Ilie Onica ◽

Dacian Marian

Keyword(s):

Surface Deformation ◽

Longwall Mining ◽

Underground Mining ◽

Ground Surface ◽

Surface Subsidence ◽

Coal Seams ◽

Elasto Plasticity ◽

Ground Surface Subsidence ◽

Time Dependent Analysis

Abstract In the case of the thick and gentle coal seam no. 3 of the Jiu Valley Coal Basin (Romania), the mining methods are by use of the longwall mining technologies with roof control by caving or top coal caving. In this paper, it is presented the analysis of the complex deformations of the ground surface, over time, as a consequence of the coal mining in certain mining fields of the basin. Also, it is analysed the ground surface subsidence phenomenon using the CESAR-LCPC finite element code. The modelling is made in the elasticity and the elasto-plasticity behaviour hypothesis. Also, the time dependent analysis of the ground surface deformation was achieved with the aid of an especial profile function. The obtained results are compared with the in situ measurements data basis.

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

Computers & Geosciences ◽

10.1016/j.cageo.2014.03.014 ◽

2014 ◽

Vol 68 ◽

pp. 38-52 ◽

Cited By ~ 102

Author(s):

Adrián J. Riquelme ◽

A. Abellán ◽

R. Tomás ◽

M. Jaboyedoff

Keyword(s):

Rock Mass ◽

Point Clouds ◽

New Approach ◽

3D Point Clouds

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

Remote Sensing ◽

10.3390/rs13152894 ◽

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.

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

IEEE Access ◽

10.1109/access.2017.2771201 ◽

2017 ◽

Vol 5 ◽

pp. 26734-26742 ◽

Cited By ~ 3

Author(s):

Xianquan Han ◽

Shengmei Yang ◽

Fangfang Zhou ◽

Jian Wang ◽

Dongbo Zhou

Keyword(s):

Rock Mass ◽

Point Clouds ◽

Terrestrial Lidar ◽

3D Point Clouds

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Accuracy Assessment of Cultural Heritage Models Extracting 3D Point Cloud Geometric Features with RPAS SfM-MVS and TLS Techniques

Drones ◽

10.3390/drones5040145 ◽

2021 ◽

Vol 5 (4) ◽

pp. 145

Author(s):

Alessandra Capolupo

Keyword(s):

Cultural Heritage ◽

Point Cloud ◽

Accuracy Assessment ◽

Laser Scanner ◽

Point Clouds ◽

Geometric Features ◽

3D Point Cloud ◽

3D Point Clouds ◽

Research Activities ◽

The Impact

A proper classification of 3D point clouds allows fully exploiting data potentiality in assessing and preserving cultural heritage. Point cloud classification workflow is commonly based on the selection and extraction of respective geometric features. Although several research activities have investigated the impact of geometric features on classification outcomes accuracy, only a few works focused on their accuracy and reliability. This paper investigates the accuracy of 3D point cloud geometric features through a statistical analysis based on their corresponding eigenvalues and covariance with the aim of exploiting their effectiveness for cultural heritage classification. The proposed approach was separately applied on two high-quality 3D point clouds of the All Saints’ Monastery of Cuti (Bari, Southern Italy), generated using two competing survey techniques: Remotely Piloted Aircraft System (RPAS) Structure from Motion (SfM) and Multi View Stereo (MVS) techniques and Terrestrial Laser Scanner (TLS). Point cloud compatibility was guaranteed through re-alignment and co-registration of data. The geometric features accuracy obtained by adopting the RPAS digital photogrammetric and TLS models was consequently analyzed and presented. Lastly, a discussion on convergences and divergences of these results is also provided.

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