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Assessment of the Effect of Blast Hole Diameter on the Number of Oversize Boulders Using ANN Model

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Abstract

Now-a-days, blasts are planned using large diameter blast holes. The loading density (kg/m) and subsequently the energy available for the breakage of the rockmass increase with the diameter. The in-hole velocity of detonation (VoD) of non-ideal explosive also boosts up with the increase in diameter till the optimum diameter is reached. The increase in the energy content and in-hole VoD cause a sizable effect on the rock fragmentation. The effect can be assessed by counting the number of oversize boulders. This paper explains as to how the technique of artificial neural network modeling was used to predict the number of oversize boulders resulting from ANFO and SME blasts with blast holes of different diameters. The results from ANFO blasts indicated that there was no significant variation in the number of oversize boulders with the diameter whereas a perceptible variation was noticed in case of SME blasts with the change in the diameter. The change in the number of oversize boulders in ANFO blasts was negligible because mean energy factor remained almost same even when the diameter of the blast holes was altered. The decrease in the number of oversize boulders in SME blasts was on account of increase in mean energy factor when the blast hole diameter was increased. The increase in the in-hole VoD due to increase in the diameter of the hole was not found to have an effect on the generation of oversize boulders as this increase was not substantial both in SME and ANFO blasts.

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References

  1. J. Aler, J. Du Mouza, M. Arnould, Measurement of the fragmentation efficiency of rock mass blasting and its mining applications. Int. J. Rock. Mech. Min. Sci. Geomech. Abstr. 33(2), 125 (1996)

    Article  Google Scholar 

  2. S. Bhandari, Engineering Rock Blasting Operations (A. A. Balkema, Rotterdam, 1997)

    Google Scholar 

  3. J.T. Castro, A.V. Liste, A.S. Gonzalez, Blasting index for exploitation of aggregates, in Proceedings of the 7th Mine Planning and Equipment Selection Symposium, Calgary, Canada, 6–9 Oct 1998, p. 165

  4. A.K. Chakraborty, A.K. Raina, M. Ramulu, P.B. Choudhury, A. Haldar, P. Sahu, C. Bandopadhyay, Parametric study to develop guidelines for blast fragmentation improvement in jointed and massive formations. Eng. Geol. 73(1–2), 105 (2004)

    Article  Google Scholar 

  5. S. Esen, I. Onederra, H.A. Bilgin, Modelling the size of crushing zone around a blast hole. Int. J. Rock Mech. Min. Sci. 40(4), 485 (2003)

    Article  Google Scholar 

  6. A. Ghosh, J.J.K. Daemen, D Vanzyl, Fractal based approach to determine the effect of discontinuities on blast fragmentation, rock mechanics contributions and challenges, in Proceedings of the 31st U.S. Symposium, Golden, Colorado, 18–20 Jun 1990, p. 905

  7. C. Grundstrom, S. Kanchibotla, A. Jankovic, D.M. Thornton, blast fragmentation for maximizing the SAG mill throughput at Porgera Goldmine, in Proceedings of the 27th Annual Conference on Explosives and Blasting Technique, Orlando, Florida, 28–31 Jan 2001, p. 383

  8. E. Hamdi, J. Du Mouza, A methodology for rock mass characterization and classification to improve blast results. Int. J. Rock Mech. Min. Sci. 42(2), 177 (2005)

    Article  Google Scholar 

  9. E. Hamdi, J. Du Mouza, J.A. Fleurisson, Evaluation of the part of blasting energy used for rock mass fragmentation. Fragblast 5(3), 180 (2001)

    Article  Google Scholar 

  10. H. Hjelmberg, Some ideas on how to improve calculations of the fragment size distribution in bench blasting, in 1st International Symposium on Rock Fragmentation by Blasting, Luleå University Technology, Luleå, Sweden, 23–26 Aug 1983, p. 469

  11. C.L. Jimeno, E.L. Jimeno, F.J.A. Carcedo, Drilling and Blasting of Rocks (A.A. Balkema, Rotterdam, 1995)

    Google Scholar 

  12. N. Mojtabai, I.W. Farmer, J.P. Savely, Optimisation of rock fragmentation in bench blasting, rock mechanics contributions and challenges, in Proceedings of the 31st U.S. Symposium, Golden, Colorado, 18–20 Jun 1990, p. 897

  13. F. Ouchterlony, B. Niklasson, S. Abrahamsson, Fragmentation monitoring of production blasts at Mrica, in International Symposium on Rock Fragmentation by Blasting, FragBlast 3, Brisbane, Australia, 26–31 Aug 1990, p. 283

  14. P. Pal Roy, Breakage assessment through cluster analysis of joint set orientations of exposed benches of opencast mines. Geotech. Geol. Eng. 13(2), 79 (1995)

    Article  Google Scholar 

  15. P. Rai, S.S. Baghel, Investigation of firing patterns on fragmentation in an indian opencast limestone mine. Quarry Manag. J. 31, 33 (2004)

    Google Scholar 

  16. J.A. Sanchidrian, P. Segarra, L.M. Lopez, Energy components in rock blasting. Int. J. Rock Mech. Min. Sci. 44(1), 130 (2007)

    Article  Google Scholar 

  17. N.R. Thote, D.P. Singh, in Effect of Air-Decking on Fragmentation: A Few Case Studies of Indian Mining, Explosive & Blasting Technique, ed. by R. Holmberg (Balkema, Rotterdam, 2000), p. 265

    Google Scholar 

  18. G.K. Pradhan, Energy factor based explosive selection and performance evaluation of blasting in surface mines, Ph. D. Thesis submitted to VNIT, Nagpur, India, 2011

  19. NIRM, Evaluation of Explosives Performance using In-the-hole Detonation Velocity Measurement, National Institute of Rock Mechanics, Kolar Gold Fields, India, S&T Project funded by Ministry of Coal, Govt. of India, 2001

  20. M. Pradhan, Investigations into the effects of some factors on detonation velocity of chemically sensitised bulk emulsion explosives, Ph.D. Thesis submitted to Pt. Ravi Shankar Shukla University, Raipur, India, 2007

  21. M. Khandelwal, T.N. Singh, Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach. J. Sound Vib. 289(4–5), 711 (2006)

    Article  Google Scholar 

  22. M. Monjezi, M. Hasanipanah, M. Khandelwal, Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran by artificial neural network. Neural Comput. Appl. 22(7–8), 1637 (2013)

    Article  Google Scholar 

  23. M. Monjezi, M. Ghafurikalajahi, A. Bahrami, Prediction of blast-induced ground vibration using artificial neural networks. Tunn. Undergr. Space Technol. 26(1), 46 (2011)

    Article  Google Scholar 

  24. T.N. Singh, A study of blast induced ground vibration at Dharapani Magnesite Mine, Pitthoragarh Himalaya, India, in Proceedings of the 3rd International Symposium on Headwater Control, New Delhi, 6–8 Oct 1995, p. 183

  25. C. Sawmliana, P. Pal Roy, R.K. Singh, T.N. Singh, Blast induced air overpressure and its prediction using artificial neural network, mining technology. Trans. Inst. Min. Metall. A 116(2), 41 (2007)

    Google Scholar 

  26. M. Monjezi, H. Dehghan, F. Samimi Namin, Application of TOPSIS method in controlling fly rock in blasting operations, in Proceedings of the 7th International Science Conference SGEM, Sofia, Bulgaria, 11–15 Jun 2007, p. 41

  27. M. Monjezi, A. Mehrdanesh, A. Malek, M. Khandelwal, Evaluation of effect of blast design parameters on flyrock using artificial neural networks. Neural Comput. Appl. 23(2), 349 (2013)

    Article  Google Scholar 

  28. R. Trivedi, T.N. Singh, A.K. Raina, Prediction of blast induced fly rock in indian limestone mines using neural networks. J. Rock Mech. Geotech. En. 6, 447 (2014)

    Article  Google Scholar 

  29. W. Gate, L. Ortiz, R. Florez, Analysis of rockfall and blasting backbreak problems, in proceedings of the american rock mechanics conference, vol 4, Anchorage, Alaska, 25–29 June 2005, p. 290

  30. A.R. Karami, H. Mansouri, M.A.E. Farsangi, H. Nezamabadi, Backbreak prediction due to bench blasting: an artificial neural network approach. J. Min. Met. Fuels India 54(12), 418 (2006)

    Google Scholar 

  31. M. Monjezi, S.M.H. Rizi, V.J. Majd, M. Khandelwal, Artificial neural network as a tool for backbreak prediction. Geotech. Geol. Eng. 32(1), 21 (2014)

    Article  Google Scholar 

  32. 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, 318 (2013)

    Article  Google Scholar 

  33. Y. Jong, C. Lee, Application of neural networks to prediction of powder factor and peak particle velocity in tunnel blasting, in Proceedings of the 28th Annual Conference on Explosives and Blasting Technique, vol 2, Las Vegas, Nevada, 10–13 Feb 2002, p. 67

  34. Y.H. Jong, C.I. Lee, Influence of geological conditions on the powder factor for tunnel blasting. Int. J. Rock Mech. Min. Sci. 41(41), 533 (2004)

    Article  Google Scholar 

  35. S.S. Leu, S.F. Lin, C.K. Chen, S.W. Wang, Analysis of powder factors for tunnel blasting using neural networks. Fragblast 2(4), 433 (1998)

    Article  Google Scholar 

  36. M. Monjezi, A. Yazdian, S.M. Hesami, Use of back propagation neural network to estimate burden in tunnel blasting. J. Min. Met. Fuels 12(4), 424 (2006)

    Google Scholar 

  37. M. Monjezi, T.N. Singh, M. Khandelwal, S. Sinha, V. Singh, I. Hosseini, Prediction and analysis of blast parameters using artificial neural network. Noise Vib. Worldw 5(5), 8 (2006)

    Article  Google Scholar 

  38. A.S. Tawadrou, P.D. Katsabani, Prediction of surface blast patterns in limestone quarries using artificial neural networks. Fragblast 9(4), 233 (2005)

    Article  Google Scholar 

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

    Article  Google Scholar 

  40. P.H.S.W. Kulatilake, W. Qiong, T. Hudaverdi, C. Kuzu, Mean particle size prediction in rock blast fragmentation using neural networks. Engineering Geology 114(3–4), 298 (2010)

    Article  Google Scholar 

  41. M.J. Moghadam, M.A.E. Farsangi, H. Mansouri, H. Nezamabadi, Muck-pile fragmentation prediction using artificial neural networks. J. Min. Met. Fuels India 54(12), 421 (2006)

    Google Scholar 

  42. 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 (2010)

    Article  Google Scholar 

  43. K. Oraee, B. Asi, Prediction of rock fragmentation in open pit mines using neural network analysis, in Proceedings of 15th International Symposium on Mine Planning and Equipment Selection (MPES 2006), 20–22 Sept 2006, Torino, Italy, p. 19

  44. 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 (2013)

    Article  Google Scholar 

  45. H.B. Zhu, L. Wu, Application of gray correlation analysis and artificial neural network in rock mass blasting. J. Coal Sci. Eng. 11(1), 44 (2005)

    Google Scholar 

  46. J. Hall, I. Brunton, Critical comparison of Kruttschnitt Mineral Research Center (JKMRC) blast fragmentation models. Fragblast 6(2), 207 (2002)

    Article  Google Scholar 

  47. R. F. Chiappetta, Choosing the right delay timing for the blasting application, optimization and maintaining field control, in Proceedings of 8th High-Tech Seminar on State-of-the Art, Blasting Technology, Instrumentation and Explosives Applications, Nashville, Allentown PA, USA, 20–24 July 1998, p. 215.

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Authors and Affiliations

  1. Department of Mining Engineering, National Institute of Technology, Raipur, 492010, India

    Prakash Dhekne, Manoj Pradhan & Ravi Krishnarao Jade

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  1. Prakash Dhekne
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  2. Manoj Pradhan
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Correspondence to Prakash Dhekne.

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Dhekne, P., Pradhan, M. & Jade, R.K. Assessment of the Effect of Blast Hole Diameter on the Number of Oversize Boulders Using ANN Model. J. Inst. Eng. India Ser. D 97, 21–31 (2016). https://doi.org/10.1007/s40033-015-0083-7

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  • DOI: https://doi.org/10.1007/s40033-015-0083-7

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