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Prediction of rock fragmentation due to blasting using artificial neural network

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Abstract

Prediction of rock fragmentation is essential for optimizing blasting operation. Fragmentation depends on many parameters such as rock mass properties, blast geometry and explosive properties. In this paper, artificial neural network (ANN) method is implemented to develop a model to predict rock fragmentation due to blasting in an iron ore mine. In the developing of the proposed model eight parameters such as hole diameter, burden, powder factor, blastability index, etc., were incorporated. Training of the model was performed by back-propagation algorithm using 220 datasets. A four-layer ANN was found to be optimum with architecture 10-9-7-1. Sensitivity analysis revealed that the most effective parameters on rock fragmentation are blastability index (G), charge per delay (J), burden (C), SMR (F) and powder factor (E).

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References

  1. Bozic B (1998) Control of fragmentation by blasting. Min Petrol Eng Bull 10:49–57

    Google Scholar 

  2. Morin MA, Francesco F (2006) Monte Carlo simulation as a tool to predict blasting fragmentation based on the Kuz–Ram model. J Comput Geosci 32:352–359

    Article  Google Scholar 

  3. Thornton D, Kanchibolta SS, Brunton I (2002) Modelling the impact and blast design variation on blast fragmentation. Int J Blast Fragment 6(2):169–188

    Google Scholar 

  4. Hustrulid W (1999) Blasting principles for open pit mining, vol 1. Balkema, Rotterdam

    Google Scholar 

  5. Kanchibotla S (2001) Optimum blasting? Is it minimum cost per broken rock or maximum value per broken rock? In: Proceedings of Explo, Australasian Institute of Mining and Metallurgy

  6. Michaux S, Djordjevic N (2005) Influence of explosive energy on the strength of the rock fragments and SAG mill throughput. Int J Miner Eng 18:439–448

    Article  Google Scholar 

  7. Tawadrous A (2006) Evaluation of artificial neural networks as a reliable tool in blast design. Int Soc Explos Eng 1:1–12

    Google Scholar 

  8. Khandelwal M, Roy MP, Singh PK (2004) Application of artificial neural network in mining industry. Indian Min Eng J 43:19–23

    Google Scholar 

  9. Khandelwal M, Singh TN (2005) Prediction of blast induced air overpressure in opencast mine. J Noise Vib Worldw 36:7–16

    Article  Google Scholar 

  10. Monjezi M, Dehghani H (2008) Evaluation of effect of blasting pattern parameters on back break using neural networks. Int J Rock Mech Min Sci 45:1446–1453

    Article  Google Scholar 

  11. Khandelwal M, Singh TN (2006) Prediction of blast induced ground vibrations and frequency in opencast mine–a neural network approach. J Sound Vib 289:711–725

    Article  Google Scholar 

  12. Singh VK, Singh D, Singh TN (2001) Prediction of strength properties of some schistose rock. Int J Rock Mech Min Sci 38:269–284

    Article  Google Scholar 

  13. Kahraman S, Altun H, Tezekici BS, Fener M (2006) Sawability prediction of carbonate rocks from shear strength parameters using artificial neural networks. Int J Rock Mech Min Sci 43:157–164

    Article  Google Scholar 

  14. Fener M, Kahraman S, Ozder MO (2007) Performance prediction of circular diamond saws from mechanical rock properties in cutting carbonate rocks. J Rock Mech Rock Eng 40:505–517

    Article  Google Scholar 

  15. Monjezi M, Rezaei M, Yazdian A (2009) Prediction of backbreak in open-pit blasting using fuzzy set theory. J Expert Syst Appl 37:2637–2643

    Article  Google Scholar 

  16. Snedecor GW, Cochran WG (1956) Statistical methods applied to experiments in agriculture and biology, 5th edn. Iowa State University Press, Ames, Iowa

    Google Scholar 

  17. Student (1923) On testing varieties of cereals. Biometrika 15:271–293

    Article  Google Scholar 

  18. Levitt SD, List JA (2009) Field experiments in economics: the past, the present, and the future. Eur Econ Rev 53:1–18

    Article  Google Scholar 

  19. Wasserman PD (1989) Neural computing theory and practice. Van Nostrand Reinhold, New York

    Google Scholar 

  20. Limpon RP (1987) An introduction to computing with neural nets. IEEE ASSP Magazine, pp 4–22

  21. Kosko B (1958) Neural networks and fuzzy system, a dynamical system approach to machine intelligence. Prentice–Hall, Englewood Cliffs

    Google Scholar 

  22. Nielsen RH (1998) Neurocomputing, picking the human brain. IEEE 3:36–41

    Google Scholar 

  23. Neaupanea KM, Achet SH (2004) Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya. J Eng Geol 74:213–226

    Article  Google Scholar 

  24. Anderson JA (1995) An introduction to neural networks. MIT Press, Cambridge, MA, pp 672. ISBN: 10:0262011441

  25. Chauvin Y, Rumelhart DE (1995) Backpropagation: theory, architectures and applications. Erlbaum, Mahwah, NJ, pp 561. ISBN: 080581258X

  26. Alsmadi MKS, Omar KB, Noah SA (2009) Back propagation algorithm: the best algorithm among the multi-layer perceptron algorithm. Int J Comput Sci Netw Secur 9:378–383

    Google Scholar 

  27. Chandok JS, Kar IN, Tuli S (2008) Estimation of furnace exit gas temperature (FEGT) using optimized radial basis and back-propagation neural networks. J Rock Energy Convers Manag 49:1989–1998

    Article  Google Scholar 

  28. Monjezi M, Yazdian A, Hesami SM (2006) Use of back propagation neural network to estimate burden in tunnel blasting. J Mines Metals Fuels 54:424–429

    Google Scholar 

  29. Monjezi M, Bahrami A, Yazdian A, Sayadi AR (2009) Prediction and controlling of flyrock in blasting operation using artificial neural network. Arab J Geoscis. doi:10.1007/s12517-009-0091-8

  30. Monjezi, M, Bahrami A, Yazdian A (2009) Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int J Rock Mech Min Sci. doi:10.1016/j.ijrmms.2009.09.008

  31. Yang Y, Zhang Q (1997) A hierarchical analysis for rock engineering using artificial neural networks. J Rock Mech Rock Eng 30:207–222

    Article  Google Scholar 

  32. Lu Y (2005) Underground blast induced ground shock and its modeling using artificial neural network. J Comput Geotech 32:164–178

    Article  Google Scholar 

  33. Jong YH, Lee CI (2004) Influence of geological conditions on the powder factor for tunnel blasting. Int J Rock Mech Min Sci 41:533–538

    Article  Google Scholar 

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Authors and Affiliations

  1. Tarbiat Modares University, Tehran, Iran

    A. Bahrami, M. Monjezi, K. Goshtasbi & A. Ghazvinian

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  1. A. Bahrami
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  2. M. Monjezi
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  3. K. Goshtasbi
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  4. A. Ghazvinian
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Correspondence to M. Monjezi.

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Bahrami, A., Monjezi, M., Goshtasbi, K. et al. Prediction of rock fragmentation due to blasting using artificial neural network. Engineering with Computers 27, 177–181 (2011). https://doi.org/10.1007/s00366-010-0187-5

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  • DOI: https://doi.org/10.1007/s00366-010-0187-5

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