RETRACTED: Rock Fragmentation Classification Applying Machine Learning Approaches

Samaneh Liaghat, Nadhir Al Ansari, Armin Iravani

Environmental and Natural Resources Engineering Department, Luleå University of Technology, Luleå, Sweden.

DOI: 10.4236/eng.2023.156030                

Abstract

Short Retraction Notice

The paper does not meet the standards of "Engineering".

This paper has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's Retraction Guidelines. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused.

Editor guiding this retraction: Prof. David L. Carroll (Editor-in-Chief of ENG)

Keywords

Rock Blasting, Load Dump Haul, Image Analysis, Artificial Intelligence, Mine Productivity

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

References

[1]	Thomas, B., Kamran, E. and Schoellig, A.P. (2017) A Real-Time Analysis of Post- Blast Rock Fragmentation Using UAV Technology. International Journal of Mining, Reclamation and Environment, 31, 439-456. https://doi.org/10.1080/17480930.2017.1339170
[2]	Tavakol, E.A. and Mehdi, H. (2017) Analysis of Blasted Rocks Fragmentation Using Digital Image Processing (Case Study: Limestone Quarry of Abyek Cement Company). International Journal of Geo-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. Mining Science and Technology, 11, 19-36. https://doi.org/10.1016/0167-9031(90)80003-Y
[4]	Mostafa, B., Hasan K. and Ali, S. (2013) Prediction of Fragment Size Distribution from Blasting: Artificial Neural Networks Approch. Proceedings of 36th APCOM Symposium Applications of Computers and Operations Research in the Mineral Industry, Porto Alegre, 4-8 November 2013.
[5]	Sang H.C. and Kaneko, K. (2004) Rock Fragmentation Control in Blasting. Materials Transactions, 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. Journal of Geotechnical Engineering, 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. Proceedings of 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.I., Ami Shah, S.M.A. and Behan, M.Y. (2009) Measurement of Size Distribution of Blasted Rock Using Digital Image Processing. JKAU: Journal of King Abdulaziz University, 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: International Engineering Journal, 72, 141-146. https://doi.org/10.1590/0370-44672017720125
[10]	Onederra, I., Thurley, M.J. and Catalan, A. (2015) 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. Minerals Engineering, 14, 689-700. https://doi.org/10.1016/S0892-6875(01)00065-6
[12]	Roy, M.P. Paswan, R.K. Sarim, M., Kumar, S., Jha, R.R. and Singh, P.K. (2016) Rock Fragmentation by Blasting—A Review. Journal of Mines, Metals and Fuels, 64, 424-431.
[13]	Mohammad, B., Mohammad, A., Farhang, S., Farzad, S. and Sadjad, M. (2019) A New Framework for Evaluation of Rock Fragmentation in Open Pit Mines. Journal of Rock Mechanics and Geotechnical Engineering, 11, 325-336. https://doi.org/10.1016/j.jrmge.2018.11.006
[14]	Daniel, J. and Ouchterlony, F. (2011) Fragmentation in Small-Scale Confined Blasting. International Journal of Mining and Mineral Engineering, 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. Proceedings of 6th International Conference & Exhibition on Mass Mining, Sudbury, 10-14 June 2012, 1-10.
[16]	Sanchidrián, J.A., Finn, O., Pablo, S. and Peter, M. (2014) Size Distribution Functions for Rock Fragments. International Journal of Rock Mechanics and Mining Sciences, 71, 381-394. https://doi.org/10.1016/j.ijrmms.2014.08.007
[17]	Ali, B., Daniel, J. and Hakan, S. (2017) Target Fragmentation for Efficient Loading and Crushing—The Aitik Case. Journal of the Southern African Institute of Mining and Metallurgy, 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. Proceedings of the 9th International Symposium on Rock Fragmentation by Blasting, 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. Computers in Industry, 61, 391-408. https://doi.org/10.1016/j.compind.2009.10.003
[20]	Andreas, G., Potsch M. and Schubert, W. (2017) Digital Rock Mass Characterization 2017—Where Are We Now? What Comes Next? Geomechanics and Tunnelling, 10, 561-566. https://doi.org/10.1002/geot.201700036
[21]	Campnell, A.D. and Thurley, M.J. (2017) Application of Laser Scanning to Measure Fragmentation in Underground Mines. Mining Technology, 126, 240-247.
[22]	Chmelina, K., Gaich, A., Keuschnig, M., Delleske, R., Wenighofer, R. and Galler, R. (2019) Drone Based Deformation Monitoring at the Zentrum am Berg Tunnel Project, Austria. Results and Findings 2017-2019. In: Peila, D., Viggiani, G. and Celestino, T., Eds., Tunnels and Underground Cities. Engineering and Innovation Meet Archaeology, Architecture and Art, CRC Press, London, 701-710. https://doi.org/10.1201/9780429424441-74
[23]	Markus, D., Rajib, G., Navarro, M.J. and Hakan, S. (2017) Utilizing Production Data to Predict Operational Disturbances in Sublevel Caving. Proceeding of the 26th International Symposium on Mine Planning and Equipment Selection Lulea (MPES 2017), Sweden, 29-31 August 2017, 139-144.
[24]	Cracknell, M.J. and Reading, A.M. (2014) Geological Mapping Using Remote Sensing Data: A Comparison of Five Machine Learning Algorithms, Their Response to Variations in the Spatial Distribution of Training Data and the Use of Explicit Spatial Information. Computers and Geosciences, 63, 22-33. https://doi.org/10.1016/j.cageo.2013.10.008
[25]	Maitre, J., Kévin, B. 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
[26]	Tessier, J., Carl, D. and Gianni, B. (2007) A Machine Vision Approach to On-Line Estimation of Run-of-Mine Ore Composition on Conveyor Belts. Minerals Engineering, 1129-1144. https://doi.org/10.1016/j.mineng.2007.04.009
[27]	Perez, C.A., Estévez, P.A., Vera, P.A., Castillo, L.E., Aravena, C.M., Schulz, D.A. and Medina, L.E. (2011) Ore Grade Estimation by Feature Selection and Voting Using Boundary Detection in Digital Image Analysis. International Journal of Mineral Processing, 20, 28-36. https://doi.org/10.1016/j.minpro.2011.07.008
[28]	Yaghoobi, H., Hamid, M., Ali Mohammad, E.F. and Nezamabadi-Pour, H. (2019) Determining the Fragmented Rock Size Distribution Using Textural Feature Extraction of Images. Powder Technology, 342, 630-641. https://doi.org/10.1016/j.powtec.2018.10.006
[29]	Rajib, G., Markus, D., Anna, G., Hanna, F. and Hakan, S. (2017) Assessment of Rock Mass Quality Using Drill Monitoring Technique for Hydraulic ITH Drills. International Journal of Mining and Mineral Engineering, 8, 169-186. https://doi.org/10.1504/IJMME.2017.10006862
[30]	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. Proceedings of Fifth International Conference on Block and Sublevel Caving, Australian Centre for Geomechanics (Caving 2022), Perth, 15-19 May 2023, 381-392. https://doi.org/10.36487/ACG_repo/2205_26
[31]	Tom, B. (2001) What’s New with the Digital Image Analysis Software Split-Desktop?? Split Engineering, LLC, Tucson.
[32]	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 Modeling and Applications, Stony Brook, 4-6 June 2008.
[33]	Ohbuchi, R. and Furuya, T. (2008) Accelerating Bag-of-Features Sift Algorithm for 3D Model Retrieval. Proceedings of 2018 SAMT Workshop on Semantic 3D Media, Koblenz, 3-5 December 2008, 1-8.
[34]	Gao, Y. and Dai, Q. (2015) Chapter 5-View Representation. In: Gao, Y. and Dai, Q., Eds., View-Based 3-d Object Retrieval, Elsevier, Amsterdam, 67-83. https://doi.org/10.1016/B978-0-12-802419-5.00005-X
[35]	Furuya, T. and Ohbuchi, R. (2009) Dense Sampling and Fast Encoding for 3D Model Retrieval Using Bag-of-Visual Features. Proceedings of the ACM International Conference on Image and Video Retrieval (CIVR’ 09), Santorini Island, 8-10 July 2009, 1-8. https://doi.org/10.1145/1646396.1646430
[36]	Wagstaff, K., Cardie, C., Rogers, S. and Schrodl, S. (2001) Constrained k-Means Clustering with Background Knowledge. Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williamstown, 28 June-1 July 2001, 577-584.
[37]	Bay, H., Ess, A., Tuytelaars, T. and Van, L.G. (2006) SURF: Speeded up Robust Features. In: Leonardis, A., Bischof, H. and Pinz, A., Eds., ECCV 2006: Computer Vision-ECCV 2006, Lecture Notes in Computer Science, Vol. 3951, Springer, Berlin, 404-417. https://doi.org/10.1007/11744023_32
[38]	Nikam, S.S. (2015) A Comparative Study of Classification Techniques in Data Mining Algorithms. Oriental Journal of Computer Science & Technology, 8, 13-19.
[39]	Cortes, C. and Vapnik, V. (1995) Support-Vector Networks. Support-Vector Networks, 20, 273-297. https://doi.org/10.1007/BF00994018
[40]	Sergios, T. and Konstantinos, K. (2008) Pattern Recognition, Fourth Ed., Elsevier, San Diego, California.
[41]	Bishop, C.M. (2006) Pattern Recognition and Machine Learning. Springer, Berlin.
[42]	Rokach, L. (2010) Ensemble-Based Classifiers. Artificial Intelligence Review, 33, 1-39. https://doi.org/10.1007/s10462-009-9124-7
[43]	Kam, H.T. (1998) The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 832-844. https://doi.org/10.1109/34.709601
[44]	Altman, N.S. (1992) An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. The American Statistician, 46, 175-185. https://doi.org/10.1080/00031305.1992.10475879
[45]	Magerman, D.M. (1995) Statistical Decision-Tree Models for Parsing. Proceedings of the 33rd Annual Meeting on Association for Computational Linguistics (ACL’ 95), Cambridge, 26-30 June 1995, 276-283. https://doi.org/10.3115/981658.981695
[46]	Kamiński, B., Jakubczyk, M. and Szufel, P. (2017) A Framework for Sensitivity Analysis of Decision Trees. Central European Journal of Operations Research, 26, 135-159. https://doi.org/10.1007/s10100-017-0479-6
[47]	Cawley, G.C. and Talbot, N.L. (2010) On Over-Fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation. Journal of Machine Learning Research, 11, 2079-2107.
[48]	Wimmer, M., Anders, N., Edoardo, R., Nikolaos, P. and Matthew, T. (2015) Analysis of Rock Fragmentation and Its Effect on Gravity Flow at the Kiruna Sublevel Caving Mine. Proceedings of 11th International Symposium on Rock Fragmentation by Blasting: FragBlast11, Carlton VIC: The Australasian Institute of Mining and Metallurgy, Sydney, 24-26 August 2015, 775-791.