The nature of rock fragmentation affects the downstream mining processes like loading, hauling, and crushing the blasted rock. Therefore, it is important to evaluate rock fragmentation after blasting for choosing or designing optimal strategies for these processes. However, current techniques of rock fragmentation analysis such as sieving, image-based analysis, empirical methods or artificial intelligence-based methods entail different practical challenges, for example, excessive processing time, higher costs, applicability issues in underground environments, user-biasness, accuracy issues, etc. A classification model has been developed by utilizing image analysis techniques to overcome these challenges. The model was tested on about 7500 videos of load-haul-dump (LHD) buckets with blasted material from Malmberget iron ore mine in Sweden. A Kernel-based support vector machine (SVM) method was utilized to extract frames comprising loaded LHD buckets. Then, the blasted rock in the buckets was classified into five distinct categories using the bagging k-nearest neighbor (KNN) technique. The results showed 99.8% and 89.8% accuracy for kernel-based SVM and bagging KNN classifiers, respectively. The developed framework is efficient in terms of the operation time, cost and practicability for different mines and variate amounts of rock masses.
References
[1]
Bamford, T., Esmaeili, K. and Schoellig, A.P. (2017) A Real-Time Analysis of Post-Blast Rock Fragmentation Using UAV Technology. InternationalJournalofMining, ReclamationandEnvironment, 31, 439-456. https://doi.org/10.1080/17480930.2017.1339170
[2]
Tavakol Elahi, A. and Hosseini, M. (2017) Analysis of Blasted Rocks Fragmentation Using Digital Image Processing (Case Study: Limestone Quarry of Abyek Cement Company). InternationalJournalofGeo-Engineering, 8, Article No. 16. https://doi.org/10.1186/s40703-017-0053-z
[3]
Hunter, G.C., McDermott, C., Miles, N.J., Singh, A. and Scoble, M.J. (1990) A Review of Image Analysis Techniques for Measuring Blast Fragmentation. MiningScienceandTechnology, 11, 19-36. https://doi.org/10.1016/0167-9031(90)80003-y
[4]
Badroddin, M., Khoshrou, H. and Siamaki, A. (2013) Prediction of Fragment Size Distribution from Blasting: Artificial Neural Networks Approach. 36th APCOM Symposium Applications of Computers and Operations Research in the Mineral Industry, Porto Alegre, 4-8 November 2013.
[5]
Cho, S.H. and Kaneko, K. (2004) Rock Fragmentation Control in Blasting. MaterialsTransactions, 45, 1722-1730. https://doi.org/10.2320/matertrans.45.1722
[6]
Kemeny, J.M., Devgan, A., Hagaman, R.M. and Wu, X. (1993) Analysis of Rock Fragmentation Using Digital Image Processing. JournalofGeotechnicalEngineering, 119, 1144-1160. https://doi.org/10.1061/(asce)0733-9410(1993)119:7(1144)
[7]
Thurley, M.J. (2013) Automated Image Segmentation and Analysis of Rock Piles in an Open-Pit Mine. 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Hobart, 26-28 November 2013, 1-8. https://doi.org/10.1109/dicta.2013.6691484
[8]
Siddiqui, F., Shah, S. and Behan, M. (2009) Measurement of Size Distribution of Blasted Rock Using Digital Image Processing. JournalofKingAbdulazizUniversity-EngineeringSciences, 20, 81-93. https://doi.org/10.4197/eng.20-2.4
[9]
Seccatore, J. (2019) A Review of the Benefits for Comminution Circuits Offered by Rock Blasting. REM—InternationalEngineeringJournal, 72, 141-146. https://doi.org/10.1590/0370-44672017720125
[10]
Onederra, I., Thurley, M.J. and Catalan, A. (2014) Measuring Blast Fragmentation at Esperanza Mine Using High-Resolution 3D Laser Scanning. Mining Technology, 124, 34-36. https://doi.org/10.1179/1743286314y.0000000076
[11]
Casali, A., Gonzalez, G., Vallebuona, G., Perez, C. and Vargas, R. (2001) Grindability Soft-Sensors Based on Lithological Composition and On-Line Measurements. MineralsEngineering, 14, 689-700. https://doi.org/10.1016/s0892-6875(01)00065-6
[12]
Roy, M.P., Paswan, R.K., Sarim, M.D., Kumar, S., Jha, R. and Singh, P.K. (2016) Rock Fragmentation by Blasting—A Review. Journal of Mines, Metals and Fuels, 64, 424-431.
[13]
Babaeian, M., Ataei, M., Sereshki, F., Sotoudeh, F. and Mohammadi, S. (2019) A New Framework for Evaluation of Rock Fragmentation in Open Pit Mines. JournalofRockMechanicsandGeotechnicalEngineering, 11, 325-336. https://doi.org/10.1016/j.jrmge.2018.11.006
[14]
Johansson, D. and Ouchterlony, F. (2011) Fragmentation in Small-Scale Confined Blasting. InternationalJournalofMiningandMineralEngineering, 3, 72-94. https://doi.org/10.1504/ijmme.2011.041450
[15]
Wimmer, M., Nordqvist, A.A., Ouchterlony, F. and Selldén, H. (2012) 3D Mapping of Sublevel Caving (SLC) Blast Rings and Ore Flow Disturbances in the LKAB Kiruna Mine. Luleå University of Technology.
[16]
Sanchidrián, J.A., Ouchterlony, F., Segarra, P. and Moser, P. (2014) Size Distribution Functions for Rock Fragments. InternationalJournalofRockMechanicsandMiningSciences, 71, 381-394. https://doi.org/10.1016/j.ijrmms.2014.08.007
[17]
Beyglou, A. (2017) Target Fragmentation for Efficient Loading and Crushing—The Aitik Case. JournaloftheSouthernAfricanInstituteofMiningandMetallurgy, 117, 1053-1062. https://doi.org/10.17159/2411-9717/2017/v117n11a10
[18]
Ouchterlony, F. (2009) Fragmentation Characterization; the Swebrec Function and Its Use in Blast Engineering. Proceedingsofthe 9thInternationalSymposiumonRockFragmentationbyBlasting, Granada, 13-17 August 2009, 3-22.
[19]
Chatterjee, S., Bhattacherjee, A., Samanta, B. and Pal, S.K. (2010) Image-Based Quality Monitoring System of Limestone Ore Grades. ComputersinIndustry, 61, 391-408. https://doi.org/10.1016/j.compind.2009.10.003
[20]
Gaich, A., Pötsch, M. and Schubert, W. (2017) Digital Rock Mass Characterization 2017—Where Are We Now? What Comes Next? GeomechanicsandTunnelling, 10, 561-566. https://doi.org/10.1002/geot.201700036
[21]
Campbell, A.D. and Thurley, M.J. (2017) Application of Laser Scanning to Measure Frag-Mentation in Underground Mines. MiningTechnology, 126, 240-247.
[22]
Danielsson, M., Ghosh, R., Navarro Miguel, J., Johansson, D. and Schunnesson, H. (2017) Utilizing Production Data to Predict Operational Disturbances in Sublevel Caving. 26th International Symposium on Mine Planning and Equipment Selection, Luleå, 29-31 August 2017, 139-144.
[23]
Maitre, J., Bouchard, K. and Bédard, L.P. (2019) Mineral Grains Recognition Using Computer Vision and Machine Learning. Computers&Geosciences, 130, 84-93. https://doi.org/10.1016/j.cageo.2019.05.009
[24]
Tessier, J., Duchesne, C. and Bartolacci, G. (2007) A Machine Vision Approach to On-Line Estimation of Run-Of-Mine Ore Composition on Conveyor Belts. MineralsEngineering, 20, 1129-1144. https://doi.org/10.1016/j.mineng.2007.04.009
[25]
Perez, C.A., Estévez, P.A., Vera, P.A., Castillo, L.E., Aravena, C.M., Schulz, D.A., etal. (2011) Ore Grade Estimation by Feature Selection and Voting Using Boundary Detection in Digital Image Analysis. International Journal of Mineral Processing, 101, 28-36. https://doi.org/10.1016/j.minpro.2011.07.008
[26]
Yaghoobi, H., Mansouri, H., Ebrahimi Farsangi, M.A. and Nezamabadi-Pour, H. (2019) Determining the Fragmented Rock Size Distribution Using Textural Feature Extraction of Images. PowderTechnology, 342, 630-641. https://doi.org/10.1016/j.powtec.2018.10.006
[27]
Danielsson, M., Söderström, E., Schunnesson, H., Gustafson, A., Fredriksson, H., Johansson, D., et al. (2022) Predicting Rock Fragmentation Based on Drill Monitoring: A Case Study from Malmberget Mine, Sweden. Journal of the Southern African InstituteofMiningandMetallurgy, 122, 1-11. https://doi.org/10.17159/2411-9717/1587/2022
[28]
Ghosh, R., Danielsson, M., Gustafson, A., Falksund, H. and Schunnesson, H. (2017) Assessment of Rock Mass Quality Using Drill Monitoring Technique for Hydraulic ITH Drills. InternationalJournalofMiningandMineralEngineering, 8, 169-186. https://doi.org/10.1504/ijmme.2017.085830
[29]
Manzoor, S., Gustafson, A., Schunnesson, H., Tariq, M. and Wettainen, T. (2022) Rock Fragmentation Measurements in Sublevel Caving: Field Tests at LKAB’s Malmberget Mine. In: Potvin, Y., Ed., Caving 2022: FifthInternationalConferenceonBlockandSublevelCaving, Australian Centre for Geomechanics, 381-392. https://doi.org/10.36487/acg_repo/2205_26
[30]
Bobo, T. (2010) What’s New with the Digital Image Analysis Software Split-Desktop®? Split Engineering, LLC.
[31]
Ohbuchi, R., Osada, K., Furuya, T. and Banno, T. (2008) Salient Local Visual Features for Shape Based 3D Model Retrieval. Proceedings of IEEE Conference on Shape ModelingandApplications, Stony Brook, 4-6 June 2008, 93-102.
[32]
Ohbuchi, R. and Furuya, T. (2008) Accelerating Bag-of-Features Sift Algorithm for 3D Model Retrieval. Proceedings of SAMT Workshop on Semantic 3D Media, Koblenz, 3 December 2008, 23-30.
[33]
Gao, Y. and Dai, Q. (2015) View-Based 3-D Object Retrieval. Morgan Kaufmann. 67-83.
[34]
Furuya, T. and Ohbuchi, R. (2009) Dense Sampling and Fast Encoding for 3D Model Retrieval Using Bag-of-Visual Features. ProceedingsoftheACMInternationalConferenceonImageandVideoRetrieval, Santorini Island, 8-10 July 2009, 1-8. https://doi.org/10.1145/1646396.1646430
[35]
Wagstaff, K., Cardie, C., Rogers, S. and Schrödl, S. (2001) Constrained k-Means Clustering with Background Knowledge. International Conference on Machine Learning, 1, 577-584.
[36]
Bay, H., Tuytelaars, T. and Van Gool, L. (2006) SURF: Speeded up Robust Features. In: Leonardis, A., Bischof, H. and Pinz, A., Eds., Computer Vision—ECCV 2006, 404-417. https://doi.org/10.1007/11744023_32
[37]
Nikam, S.S. (2015) A Comparative Study of Classification Techniques in Data Mining Algorithms. Oriental Journal of Computer Science & Technology, 8, 13-19.
[38]
Cortes, C. and Vapnik, V. (1995) Support-vector Networks. MachineLearning, 20, 273-297. https://doi.org/10.1007/bf00994018
[39]
Theodoridis, S. and Koutroumbas, K. (2008) Pattern Recognition. Fourth Edition, Elsevier.
[40]
Bishop, C.M. (2006) Pattern Recognition and Machine Learning. Springer Science+ Business Media.
[41]
Rokach, L. (2009) Ensemble-Based Classifiers. ArtificialIntelligenceReview, 33, 1-39. https://doi.org/10.1007/s10462-009-9124-7
[42]
Ho, T.K. (1998) The Random Subspace Method for Constructing Decision Forests. IEEETransactionsonPatternAnalysisandMachineIntelligence, 20, 832-844. https://doi.org/10.1109/34.709601
[43]
Altman, N.S. (1992) An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. TheAmericanStatistician, 46, 175-185. https://doi.org/10.1080/00031305.1992.10475879
[44]
Magerman, D.M. (1995) Statistical Decision-Tree Models for Parsing. Proceedingsofthe 33rd Annual MeetingonAssociationforComputationalLinguistics, Cambridge, 26-30 June 1995, 276-283. https://doi.org/10.3115/981658.981695
[45]
Kamiński, B., Jakubczyk, M. and Szufel, P. (2017) A Framework for Sensitivity Analysis of Decision Trees. CentralEuropeanJournalofOperationsResearch, 26, 135-159. https://doi.org/10.1007/s10100-017-0479-6
[46]
Cawley, G.C. and Talbot, N.L. (2010) On Over-Fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation. JournalofMachineLearningResearch, 11, 2079-2107.
[47]
Yang, Z., He, B., Liu, Y., Wang, D. and Zhu, G. (2021) Classification of Rock Fragments Produced by Tunnel Boring Machine Using Convolutional Neural Networks. AutomationinConstruction, 125, Article ID: 103612. https://doi.org/10.1016/j.autcon.2021.103612
[48]
Wimmer, M., Nordqvist, A., Righetti, E., Petropoulos, N. and Thurley, M. (2015) Analysis of Rock Fragmentation and Its Effect on Gravity Flow at the Kiruna Sublevel Caving Mine. InternationalSymposiumonRockFragmentationbyBlasting, Sydney, 24-26 August 2015, 775-791.
