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Review of Empirical and Intelligent Techniques for Evaluating Rock Fragmentation Induced by Blasting

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Environmental Issues of Blasting

Part of the book series: SpringerBriefs in Applied Sciences and Technology ((BRIEFSAPPLSCIENCES))

Abstract

Rock fragmentation, or the fragment size distribution of blasted rock of bench blasting, is crucial in excavation of any civil or mining project. The blasting operation plays a pivotal role in the overall economics of opencast mines. The blasting affects all the downstream operations, i.e. loading, transport, crushing, and milling operations. Prediction of rock fragmentation is important for practicing blasting engineer. It is well known that the rock fragmentation depends upon blast design parameters such as stiffness ratio, powder factor, and maximum charge per delay. Measurement of blast fragmentation is vital for deciding efficiency of blasting. Various blast fragmentation measurements are evolving from sieve analysis to image analysis. Challenge still remains accuracy of fragmentation vis-a-vis time and cost required for measurement and analysis of fragment size and distribution. During initial era, various empirical equations were developed for predicting fragment size based on blast design parameters. During the last decade, various machine learning (ML) models such as artificial neural network and support vector machine have been proposed for prediction of rock fragmentation. These ML models were reviewed in this study and their advantages and disadvantages were discussed. In addition, practical applications of the ML techniques for civil and mining engineers will be described in detail. This study is a useful source for those who are interested to do further research in the field of rock fragmentation induced by blasting. Theory-based or physics-based ML is a new corridor of ML techniques, which are able to bring the concept of different theories behind rock fragmentation into modeling part to have a more generalized and accurate predictive techniques.

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References

  1. S. Kanchibotla, S. Morrell, W. Valery, P. O’loughlin, Exploring the effect of blast design on SAG Mill throughput at KCGM, in Proceeding of the Mine-Mill Conference (1998), pp. 153–158

    Google Scholar 

  2. P.A. Lilly, Empirical method of assessing rock mass blastability, in Symposium Series—Australasian Institute of Mining Metallurgy (January 1986) pp. 89–92

    Google Scholar 

  3. A.K. Ghose, Design of drilling and blasting subsystems—a rock mass classification approach, in Mine Planning and Equipment Selection (1988)

    Google Scholar 

  4. J.P. Latham, P. Lu, Development of an assessment system for the blastability of rock masses. Int. J. Rock Mech. Min. Sci. 36(1), 41–55 (1999)

    Article  Google Scholar 

  5. Y. Azimi, M. Osanloo, M. Aakbarpour-Shirazi, A.A. Bazzazi, Prediction of the blastability designation of rock masses using fuzzy sets. Int. J. Rock Mech. Min. Sci. 47(7), 1126–1140 (2010)

    Article  Google Scholar 

  6. D.J. Armaghani, S. Yagiz, E.T. Mohamad, J. Zhou, Prediction of TBM performance in fresh through weathered granite using empirical and statistical approaches. Tunn. Undergr. Sp. Technol. 118, 104183 (2021)

    Google Scholar 

  7. D. Jahed Armaghani, A. Azizi, D. Jahed Armaghani, A. Azizi, Developing statistical models for solving tunnel boring machine performance problem. Appl. Artif. Intell. Tunn. Undergr. Sp. Technol. 33–53 (2021)

    Google Scholar 

  8. A. Azizi, D. Jahed Armaghani, Empirical, statistical, and intelligent techniques for TBM performance prediction, in Applications of Artificial Intelligence in Tunnelling and Underground Space Technology. SpringerBriefs in Applied Sciences and Technology (Springer, Singapore, 2021), pp. 17–32. https://doi.org/10.1007/978-981-16-1034-9_2

  9. M. Chatziangelou, B. Christaras, Blastability index on poor quality rock mass. Int. J. Civ. Eng. 2(5), 9–16 (2013)

    Google Scholar 

  10. B. Christaras, M. Chatziangelou, Blastability Quality System (BQS) for using it, in bedrock excavation. Struct. Eng. Mech. 51(5), 823–845 (2014)

    Article  Google Scholar 

  11. M. Chatziangelou, B. Christaras, A geological classification of rock mass quality and blast ability for widely spaced formations. J. Geol. Resour. Eng. 4, 160–174 (2016)

    Google Scholar 

  12. M. Koopialipoor, B.R. Murlidhar, A. Hedayat, D.J. Armaghani, B. Gordan, E.T. Mohamad, The use of new intelligent techniques in designing retaining walls. Eng. Comput. (2019). https://doi.org/10.1007/s00366-018-00700-1

  13. R. Shirani Faradonbeh et al., Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction. Int. J. Environ. Sci. Technol. 13(6) (2016)

    Google Scholar 

  14. A. Azizi, D. Jahed Armaghani, A comparative study of artificial intelligence techniques to estimate TBM performance in various weathering zones, in Applications of Artificial Intelligence in Tunnelling and Underground Space Technology. SpringerBriefs in Applied Sciences and Technology (Springer, Singapore, 2021), pp. 55–70. https://doi.org/10.1007/978-981-16-1034-9_4

  15. E. Tonnizam Mohamad, D. Jahed Armaghani, M. Hasanipanah, B.R. Murlidhar, M.N.A. Alel, Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environ. Earth Sci. 75(2), 1–15 (2016)

    Google Scholar 

  16. E.T. Mohamad, M. Koopialipoor, B.R. Murlidhar, A. Rashiddel, A. Hedayat, D.J. Armaghani, A new hybrid method for predicting ripping production in different weathering zones through in-situ tests. Measurement (2019). https://doi.org/10.1016/j.measurement.2019.07.054

  17. E.T. Mohamad, D. Li, B.R. Murlidhar, D.J. Armaghani, K.A. Kassim, I. Komoo, The effects of ABC, ICA, and PSO optimization techniques on prediction of ripping production. Eng. Comput. (2019). https://doi.org/10.1007/s00366-019-00770-9

  18. B.R. Murlidhar, D.J. Armaghani, E.T. Mohamad, Intelligence prediction of some selected environmental issues of blasting: a review. Open Constr. Build. Technol. J. 14(1), 298–308 (2020)

    Article  Google Scholar 

  19. B.R. Murlidhar, D. Kumar, D. Jahed Armaghani, E.T. Mohamad, B. Roy, B.T. Pham, A novel intelligent ELM-BBO technique for predicting distance of mine blasting-induced flyrock. Nat. Resour. Res. (2020). https://doi.org/10.1007/s11053-020-09676-6

  20. C. Yu et al., Optimal ELM–Harris Hawks optimization and ELM–grasshopper optimization models to forecast peak particle velocity resulting from mine blasting. Nat. Resour. Res. (2021). https://doi.org/10.1007/s11053-021-09826-4

  21. D.J. Armaghani, M. Hajihassani, E.T. Mohamad, A. Marto, S.A. Noorani, Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab. J. Geosci. 7(12), 5383–5396 (2014)

    Article  Google Scholar 

  22. D.J. Armaghani, E.T. Mohamad, M.S. Narayanasamy, N. Narita, S. Yagiz, Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn. Undergr. Sp. Technol. 63, 29–43 (2017)

    Article  Google Scholar 

  23. B.R. Murlidhar, D.J. Armaghani, E.T. Mohamad, S. Changthan, Rock fragmentation prediction through a new hybrid model based on imperial competitive algorithm and neural network. Smart Constr. Res. 2(3), 1–12 (2018)

    Google Scholar 

  24. S. Shams, M. Monjezi, V.J. Majd, D.J. Armaghani, Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab. J. Geosci. 8(12), 10819–10832 (2015)

    Article  Google Scholar 

  25. M. Hasanipanah, D. Jahed Armaghani, M. Monjezi, S. Shams, Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environ. Earth Sci. 75(9) (2016)

    Google Scholar 

  26. J. Zhou, C. Li, C. Arslan, M. Hasanipanah, H. Amnieh, Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting. Eng. Comput. 37(1), 265–274 (2019)

    Article  Google Scholar 

  27. J. Rosales-Huamani, R. Perez-Alvarado, U. Rojas-Villanueva, J. Castillo-Sequera, Design of a predictive model of rock breakage by blasting using artificial neural networks. Symmetry (Basel) 12(9), 1405 (2020)

    Article  Google Scholar 

  28. V. Kuznetsov, The mean diameter of the fragments formed by blasting rock. Sov. Min. Sci. 9(2), 144e8 (1973)

    Google Scholar 

  29. C. Cunningham, The Kuz-Ram model for production of fragmentation from blasting, in Proceedings of the 1st International Symposium on Rock Fragmentation by Blasting (1983), p. 439e53

    Google Scholar 

  30. P. Rosin, E. Rammler, Laws governing the fineness of powdered coal. J. Inst. Fuel 7, 29–36 (1933)

    Google Scholar 

  31. C. Cunningham, Fragmentation estimations and the Kuz-Ram model-four years on, in Proceedings of Second International Symposium on Rock Fragmentation by Blasting (1987), pp. 475–487

    Google Scholar 

  32. M. Morin, F. Ficarazzo, Monte Carlo simulation as a tool to predict blasting fragmentation based on the Kuz-Ram model. Comput. Geosci. 32(3), 352–359 (2006)

    Article  Google Scholar 

  33. S. Gheibie, H. Aghababaei, S. Hoseinie, Y. Pourrahimian, Modified Kuz—Ram fragmentation model and its use at the Sungun Copper Mine. Int. J. Rock Mech. Min. Sci. 46(6), 967–973 (2009)

    Article  Google Scholar 

  34. M. Osanloo, A. Hekmat, Prediction of shovel productivity in the Gol-e-Gohar iron mine. J. Min. Sci. 41(2), 177–184 (2005)

    Article  Google Scholar 

  35. P.F. Asl, M. Monjezi, J.K. Hamidi, D.J. Armaghani, Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm. Eng. Comput. 34(2) (2018)

    Google Scholar 

  36. O. Akyildiz, T. Hudaverdi, ANFIS modelling for blast fragmentation and blast-induced vibrations considering stiffness ratio. Arab. J. Geosci. 13(21), 1–16 (2020)

    Article  Google Scholar 

  37. H. Han, D.J. Armaghani, R. Tarinejad, J. Zhou, M.M. Tahir, Random forest and Bayesian network techniques for probabilistic prediction of flyrock induced by blasting in quarry sites. Nat. Resour. Res. 29, 655–667 (2020). https://doi.org/10.1007/s11053-019-09611-

    Article  Google Scholar 

  38. C. Xie, H. Nguyen, X. Bui, Y. Choi, J. Zhou, T. Nguyen-Trang, Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms. Geosci. Front. 12(3), 101108 (2021)

    Google Scholar 

  39. A. Mahdiyar, D.J. Armaghani, A. Marto, M. Nilashi, S. Ismail, Rock tensile strength prediction using empirical and soft computing approaches. Bull. Eng. Geol. Environ. 78(6), 4519–4531 (2019)

    Article  Google Scholar 

  40. D.F. Coates, Rock Mechanics Principles: Energy, Mines and Resources (Mines Branch, Gov. Ottawa, Canada, 1981)

    Google Scholar 

  41. F. Sereshki, S.M. Hoseini, M. Ataei, Blast fragmentation analysis using image processing. Int. J. Min. Geo-Engineering 50(2), 211–218 (2016)

    Google Scholar 

  42. A.K. Raina, A.K. Chakraborty, P.B. Choudhury, M. Ramulu, V. Udpikar, A. Sinha, Fragalyst 3.0: an indigenous fragmentation assessment tool based on digital image analysis–application and analysis. J. Mines, Met. Fuels 57(3&4), 83–88 (2009)

    Google Scholar 

  43. S. Nanda, B.K. Pal, Analysis of blast fragmentation using WipFrag. Int. J. Innov. Sci. Res. Technol. 5(6), 1561–1566 (2020)

    Article  Google Scholar 

  44. A. Saber, Effects of window-to-wall ratio on energy consumption: application of numerical and ANN approaches. J. Soft Comput. Civ. Eng. 5(4), 41–56 (2021). https://doi.org/10.22115/SCCE.2021.281977.1299

  45. R. Saisubramanian, V. Murugaiyan, Prediction of compression index of marine clay using artificial neural network and multilinear regression models. J. Soft Comput. Civ. Eng. 5(4), 114–124 (2021). https://doi.org/10.22115/SCCE.2021.287537.1324

  46. M. Koopialipoor, E.N. Ghaleini, H. Tootoonchi, D. Jahed Armaghani, M. Haghighi, A. Hedayat, Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN. Environ. Earth Sci. 78(5), 165 (2019)

    Google Scholar 

  47. V. Vapnik, S.E. Golowich, A.J. Smola, Support vector method for function approximation, regression estimation and signal processing, in Advances in Neural Information Processing Systems (1997), pp. 281–287

    Google Scholar 

  48. E. Li et al., Developing a hybrid model of salp swarm algorithm‑based support vector machine to predict the strength of fiber‑reinforced cemented paste backfill. Eng. Comput. (2020). https://doi.org/10.1007/s00366-020-01014-x

  49. M. Khandelwal, D.J. Armaghani, Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique. Geotech. Geol. Eng. 34(2), 605–620 (2016)

    Article  Google Scholar 

  50. E. Momeni, R. Nazir, D.J. Armaghani, H. Maizir, Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57, 122–131 (2014)

    Article  Google Scholar 

  51. M. Monjezi, H. Amiri, A. Farrokhi, K. Goshtasbi, Prediction of rock fragmentation due to blasting in Sarcheshmeh copper mine using artificial neural networks. Geotech. Geol. Eng. 28(4), 423–430 (2010)

    Article  Google Scholar 

  52. A. Bahrami, M. Monjezi, K. Goshtasbi, A. Ghazvinian, Prediction of rock fragmentation due to blasting using artificial neural network. Eng. Comput. 27(2), 177–181 (2011)

    Article  Google Scholar 

  53. P. Kulatilake, W. Qiong, T. Hudaverdi, C. Kuzu, Mean particle size prediction in rock blast fragmentation using neural networks. Eng. Geol. 114(3–4), 298–311 (2010)

    Article  Google Scholar 

  54. M. Monjezi, M. Rezaei, A.Y. Varjani, Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic. Int. J. Rock Mech. Min. Sci. 46(8), 1273–1280 (2009)

    Article  Google Scholar 

  55. L.A. Zadeh, Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  Google Scholar 

  56. M. Esmaeili, A. Salimi, C. Drebenstedt, M. Abbaszadeh, A.A. Bazzazi, Application of PCA, SVR, and ANFIS for modeling of rock fragmentation. Arab. J. Geosci. 8(9), 6881–6893 (2015)

    Article  Google Scholar 

  57. Q. Fang, H. Nguyen, X.-N. Bui, T. Nguyen-Thoi, J. Zhou, Modeling of rock fragmentation by firefly optimization algorithm and boosted generalized additive model. Neural Comput. Appl. 33(8), 3503–3519 (2020)

    Article  Google Scholar 

  58. S. Zhang, X.-N. Bui, N.-T. Trung, H. Nguyen, H.-B. Bui, Prediction of rock size distribution in mine bench blasting using a novel ant colony optimization-based boosted regression tree technique. Nat. Resour. Res. 29(2), 867–886 (2019)

    Article  Google Scholar 

  59. M. Hasanipanah, H. Amnieh, H. Arab, M. Zamzam, Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput. Appl. 30(4), 1015–1024 (2018)

    Article  Google Scholar 

  60. X. Shi, Z. Jian, B. Wu, D. Huang, W.E.I. Wei, Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction. Trans. Nonferrous Met. Soc. China 22(2), 432–441 (2012)

    Google Scholar 

  61. J. Huang, P. Asteris, S. Pasha, A. Mohammed, M. Hasanipanah, A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm. Eng. Comput. 1, 1–12 (2020)

    Google Scholar 

  62. B. Vergara, M. Torres, V. Aramburu, C. Raymundo, Predictive model of rock fragmentation using the neuro-fuzzy inference system (ANFIS) and Particle swarm optimization (PSO) to estimate fragmentation size in open pit mining, in Advances in Manufacturing, Production Management and Process Control, eds. by S. Trzcielinski, B. Mrugalska, W. Karwowski, E. Rossi, M. Di. Nicolantonio AHFE 2021. Lecture Notes in Networks and Systems, vol. 274 (Springer, Cham, 2021), pp. 124–131

    Google Scholar 

  63. S. Mojtahedi, I. Ebtehaj, M. Hasanipanah, H. Bonakdari, H. Amnieh, Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting. Eng. Comput. 35(1), 47–56 (2018)

    Article  Google Scholar 

  64. B. Murlidhar, D. Armaghani, E. Mohamad, S. Changthan, Rock fragmentation prediction through a new hybrid model based on imperial competitive algorithm and neural network. Smart Constr. Res. 2(1) (2018)

    Google Scholar 

  65. A. Sayadi, M. Monjezi, N. Talebi, M. Khandelwal, A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak. J. Rock Mech. Geotech. Eng. 5(4) (2013)

    Google Scholar 

  66. R. Trivedi, T. Singh, A. Raina, Prediction of blast-induced flyrock in Indian limestone mines using neural networks. J. Rock Mech. Geotech. Eng. 6(5), 447–454 (2014)

    Article  Google Scholar 

  67. H. Rad, M. Hasanipanah, M. Rezaei, A. Eghlim, Developing a least squares support vector machine for estimating the blast-induced flyrock. Eng. Comput. 34(4), 709–717 (2018)

    Article  Google Scholar 

  68. N. Ghaeini, M. Mousakhani, H.B. Amnieh, A. Jafari, Prediction of blasting fragmentation using the mutual information and rock engineering system; case study: Meydook copper mine. Int. J. Min. Geo-Eng. 51(1), 23–28 (2017)

    Google Scholar 

  69. A. Mehrdanesh, M. Monjezi, A.R. Sayadi, Evaluation of effect of rock mass properties on fragmentation using robust techniques. Eng. Comput. 34(2), 253–260 (2018)

    Article  Google Scholar 

  70. K. Sayevand, H. Arab, S.B. Golzar, Development of imperialist competitive algorithm in predicting the particle size distribution after mine blasting. Eng. Comput. 34(2), 329–338 (2017)

    Article  Google Scholar 

  71. E. Mutinda, B. Alunda, D. Maina, R. Kasomo, Prediction of rock fragmentation using the Kuznetsov-Cunningham-Ouchterlony model. J. South. African Inst. Min. Metall. 121(3), 107–112 (2021)

    Google Scholar 

  72. X. Shi, D. Huang, J. Zhou, S. Zhang, Combined ANN prediction model for rock fragmentation distribution due to blasting. J. Inf. Comput. Sci. 10(11), 3511–3518 (2013)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mining Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Ramesh M. Bhatawdekar

  2. Geotropik, Centre of Tropical Geoengineering, Department of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Ramesh M. Bhatawdekar

  3. School of Civil and Environmental Engineering, University of Technology Sydney (UTS), Sydney, Australia

    Danial Jahed Armaghani

  4. Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, Chelyabinsk, Russia

    Danial Jahed Armaghani

  5. School of Engineering, Computing and Mathematics, Oxford Brookes University, Oxford, UK

    Aydin Azizi

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  1. Ramesh M. Bhatawdekar
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  2. Danial Jahed Armaghani
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  3. Aydin Azizi
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Correspondence to Aydin Azizi .

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Bhatawdekar, R.M., Armaghani, D.J., Azizi, A. (2021). Review of Empirical and Intelligent Techniques for Evaluating Rock Fragmentation Induced by Blasting. In: Environmental Issues of Blasting. SpringerBriefs in Applied Sciences and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-16-8237-7_2

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