Ground support condition monitoring through point cloud analytics

Authors: Patwardhan, A; Karim, R

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DOI https://doi.org/10.36487/ACG_repo/2465_38

Cite As:
Patwardhan, A & Karim, R 2024, 'Ground support condition monitoring through point cloud analytics', in P Andrieux & D Cumming-Potvin (eds), Deep Mining 2024: Proceedings of the 10th International Conference on Deep and High Stress Mining, Australian Centre for Geomechanics, Perth, pp. 631-642, https://doi.org/10.36487/ACG_repo/2465_38

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Abstract:
This paper presents a methodology and the results of workflow developed to process point cloud data from underground drifts for condition monitoring of ground support. The workflow focuses on extraction and comparison of information of individual rockbolts and rockbolt neighbourhood prior to, and following, recorded seismic events. Data sources used in this methodology are point cloud data resulting from mobile LiDAR scanning and event data of blasting and microseismic events. In the first step of the workflow, locations in the drift with recorded microseismic events in the vicinity are selected. In the second step, LiDAR scans performed before and after the occurrence of one or more natural or man-made events are used to extract point cloud data within a region close to the recorded events. The extracted point cloud data is processed to compute information about the rockbolts. For each detected rockbolt, the following information is extracted: position on drift surface, tip position, angle to drift surface, length, neighbouring rockbolts, and rockbolt to neighbour’s distances. In the next phase, the rockbolt information extracted from two or more scans over the period encompassing the event are analysed. Corresponding rockbolt information from pre-event and postevent point cloud data are used to compute variation in rockbolt features. The computed variations are examined statistically and used to create a visualisation for decision support to be used by rock mechanics engineers and surveyors.

Keywords: ground support, condition monitoring, point cloud

References:
Ester, M, Kriegel, H, Xu, X & Miinchen, D 1996, ‘A density-based algorithm for discovering clusters in large spatial databases with noise’, KDD-96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, AAAI Press, Portland.
Fernandes, D, Silva, A, Névoa, R, Simões, C, Gonzalez, D, Guevara, M … & Melo-Pinto, P 2021, ‘Point-cloud based 3D object detection and classification methods for self-driving applications: a survey and taxonomy’, Information Fusion, vol. 68, pp. 161–191,
Gallwey, J, Eyre, M & Coggan, J 2020, ‘A machine learning approach for the detection of supporting rock bolts from laser scan data in an underground mine’, Tunnelling and Underground Space Technology, vol. 107,
Gigli, G & Casagli, N 2011, ‘Semi-automatic extraction of rock mass structural data from high resolution LIDAR point clouds’, International Journal of Rock Mechanics and Mining Sciences, vol. 48, pp. 187–198,
Harris, CR, Millman, KJ, van der Walt, SJ, Gommers, R, Virtanen, P, Cournapeau, D … Oliphant, TE 2020, ‘Array programming with {NumPy}’, Nature, vol. 585, pp. 357–362,
Jones, EW 2020, ‘Mobile LiDAR for underground geomechanics: learnings from the teens and directions for the twenties’, in J Wesseloo (ed.), UMT 2020: Proceedings of the Second International Conference on Underground Mining Technology, Australian Centre for Geomechanics, Perth, pp. 3-26,
Jones, E & Beck, D 2018, ‘The use of three-dimensional laser scanning for deformation monitoring in underground mines’, Proceedings of the 13th Underground Operators’ Conference, Australasian Institute of Mining and Metallurgy, Melbourne.
Lague, D, Brodu, N & Leroux, J 2013, ‘Accurate 3D comparison of complex topography with terrestrial laser scanner: application to the Rangitikei canyon (N-Z)’, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 82, pp. 10–26,
Leottau, DL, Vallejos, P & Ruiz, J 2020, LIDAR-based displacement estimation in mining applications, Automining 2018: 6th International Congress on Automation in Mining, Gecamin, Santiago.
Li, CC 2017, ‘Principles of rockbolting design’, Journal of Rock Mechanics and Geotechnical Engineering, vol. 9, pp. 396–414,
Li, S, Yue, D, Zheng, D, Cai, D & Hu, C 2022, ‘A geometric-feature-based method for automatic extraction of anchor rod points from dense point cloud’, Sensors, vol. 22, no. 23,
Martínez-Sánchez, J, Puente, I, González-Jorge, H, Riveiro, B & Arias, P 2016, ‘Automatic thickness and volume estimation of sprayed concrete on anchored retaining walls from terrestrial LIDAR data’, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 41, pp. 521–526,
Poux, F, Neuville, R, Van Wersch, L, Nys, GA & Billen, R 2017, ‘3D point clouds in archaeology: Advances in acquisition, processing and knowledge integration applied to quasi-planar objects’, Geosciences, vol. 7, no. 4,
geosciences7040096
Romero-Jarén, R & Arranz, JJ 2021, ‘Automatic segmentation and classification of BIM elements from point clouds’, Automation in Construction, vol. 124,
Rusu, RB, Blodow, N & Beetz, M 2009, ‘Fast Point Feature Histograms (FPFH) for 3D Registration’, Proceedings of the 2009 IEEE International Conference on Robotics and Automation, IEEE, Kobe, pp. 3212–3217.
ROBOT.2009.5152473
Sakurai, S 1984, ‘Displacement measurements associated with the design of underground openings’, Proceedings of the International Symposium on Field Measurements in Geomechanics, A.A. Balkema, Rotterdam, pp. 1163–1178.
Saydam, S, Liu, B, Li, B, Zhang, W, Singh, SK & Raval, S 2021, ‘A coarse-to-fine approach for rock bolt detection from 3D point clouds’, IEEE Access, vol. 9, pp. 148873–148883,
Simser, BP 2007, ‘The weakest link - ground support observations at some Canadian shield hard rock mines’, in Y Potvin (ed.), Deep Mining 2007: Proceedings of the Fourth International Seminar on Deep and High Stress Mining, Australian Centre for Geomechanics, Perth, pp. 335–348,
Singh, SK, Raval, S & Banerjee, B 2021a, ‘A robust approach to identify roof bolts in 3D point cloud data captured from a mobile laser scanner’, International Journal of Mining Science and Technology, vol. 31, no. 2, pp. 303–312,
j.ijmst.2021.01.001
Singh, SK, Raval, S & Banerjee, B 2021b. ‘Roof bolt identification in underground coal mines from 3D point cloud data using local point descriptors and artificial neural network’, Remote Sensing, vol. 42, pp. 367–377,
10.1080/2150704X.2020.1809734
Van Rossum, G 1995, Python Tutorial, Centrum voor Wiskunde en Informatica, Amsterdam.
Walton, G, Diederichs, MS, Weinhardt, K, Delaloye, D, Lato, MJ & Punkkinen, A 2018, ‘Change detection in drill and blast tunnels from point cloud data’, International Journal of Rock Mechanics and Mining Sciences, vol. 105, pp. 172–181,
Weinmann, M, Weinmann, M, Mallet, C & Brédif, M 2017, ‘A classification-segmentation framework for the detection of individual trees in dense MMS point cloud data acquired in urban areas’, Remote Sensing, vol. 9, pp. 0–28,
Xue, F, Lu, W, Chen, Z & Webster, CJ 2020, ‘From LiDAR point cloud towards digital twin city: Clustering city objects based on Gestalt principles’, Journal of Photogrammetry and Remote Sensing, vol. 167, pp. 418–431.
j.isprsjprs.2020.07.020
Yu, H, Lu, X, Cheng, G & Ge, X 2011, ‘Detection and volume estimation of mining subsidence based on multi-temporal LiDAR data’, Proceedings of the 19th International Conference of Geoinformatics, IEEE, Shanghai,
GeoInformatics.2011.5980892
Yu, H, Lu, X, Ge, X & Cheng, G 2010, ‘Digital terrain model extraction from airborne LiDAR data in complex mining area’, Proceedings of the 18th International Conference of Geoinformatics, IEEE, Beijing,
GEOINFORMATICS.2010.5567781
Zhou, Q-Y, Park, J & Koltun, V 2018, Open3D: A Modern Library for 3D Data Processing, Cornell University, Ithaca, arXiv.org/abs/1801.09847




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