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Backbreak prediction in the Chadormalu iron mine using artificial neural network

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Abstract

Backbreak is one of the unfavorable blasting results, which can be defined as the unwanted rock breakage behind the last row of blast holes. Blast pattern parameters, like stemming, burden, delay timing, stiffness ratio (bench height/burden) and rock mass conditions (e.g., geo-mechanical properties and joints), are effective in backbreak intensity. Till date, with the exception of some qualitative guidelines, no specific method has been developed for predicting the phenomenon. In this paper, an effort has been made to apply artificial neural networks (ANNs) for predicting backbreak in the blasting operation of the Chadormalu iron mine (Iran). Number of ANN models with different hidden layers and neurons were tried, and it was found that a network with architecture 10-7-7-1 is the optimum model. A comparative study also approved the superiority of the ANN modeling over the conventional regression analysis. Mean square error (MSE), variance account for (VAF) and coefficient of determination (R 2) between measured and predicted backbreak for the ANN model were calculated and found 89.46 %, 0.714 and 90.02 %, respectively. Also, for the regression model, MSE, VAF and R 2 were computed and found 66.93 %, 1.46 and 68.10 %, respectively. Sensitivity analysis was also carried out to find out the influence of each input parameter on backbreak results, and it was revealed that burden is the most influencing parameter on the backbreak, whereas water content is the least effective parameter in this regard.

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References

  1. Ambrozic T, Turk G (2003) Prediction of subsidence due to underground mining by artificial neural networks. Comput Geosci 29:627–637. doi:10.1016/S0098-3004(03)00044-X

    Article  Google Scholar 

  2. Bauer A (1982) Wall control blasting in open pits. CIM special, vol 30. In: 14th Canadian rock mechanics symposium. Canadian Institute of Mining and Metallurgy, pp 3–10

  3. Badal R (1994) Controlled blasting in jointed rocks. Int J Rock Mech Min Sci 31:79–84. doi:10.1016/0148-9062(94)92317-5

    Article  Google Scholar 

  4. Bhandari S (1997) Engineering rock blasting operations. A. A. Balkema, Rotterdam, pp 165–166

    Google Scholar 

  5. Bhandari S, Badal R (1990) Relationship of joints orientation with hole spacing parameters in multi-hole blasting. In: 3rd international symposium on rock fragmentation by blasting, Brisbane, Australia, pp 225–231

  6. Demuth H, Beal M, Hagan M (1996) Neural network toolbox 5 user’s guide. The Math Work, Natick

    Google Scholar 

  7. Gate WC, Ortiz BLT, Florez RM (2005) Analysis of rockfall and blasting backbreak problems. Proc Am Rock Mech Conf 5:671–680

    Google Scholar 

  8. Hagan TN (1980) The effect of some structural properties of rock on the design and results of blasting. In: Proceedings of the 3rd Australian–New Zealand conference. Geomechanics, pp 205–213

  9. Hafsaoui A, Talhi K (2009) Influence of joint direction and position of explosive charge on fragmentation. Arab J Sci Eng 34:125–134

    Google Scholar 

  10. Jenkins SS (1981) Adjusting blast design for best results. Pit and Quarry, Rotterdam

    Google Scholar 

  11. Jhanwar JC, Jethwa JL (2000) The use of air decks in production blasting in an open pit coal mine. Geotech Geol Eng 18:269–287. doi:10.1023/A:1016634231801

    Article  Google Scholar 

  12. Jia Z, Chen G, Huang S (1998) Computer simulation of open pit bench blasting in jointed rock mass. Int J Rock Mech Min Sci 35:476–486. doi:10.1016/S0148-9062(98)00137-5

    Article  Google Scholar 

  13. Jimeno CL, Jimeno EL, Carcedo FJA (1995) Drilling and blasting of rocks. Balkema, Rotterdam

    Google Scholar 

  14. 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. doi:10.1016/j.ijrmms.2003.12.130

    Article  Google Scholar 

  15. Kahraman S, Gunaydin O, Alber M, Fener M (2009) Evaluating the strength and deformability properties of Misis fault breccia using artificial neural networks. J Expert Syst Appl 36:6874–6878. doi:10.1016/j.eswa.2008.08.002

    Article  Google Scholar 

  16. Khandelwal M, Singh TN (2006) Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach. J Sound Vibrat 289:711–725. doi:10.1016/j.jsv.2005.02.044

    Article  Google Scholar 

  17. Khandelwal M, Monjezi M (2012) Prediction of backbreak in open pit blasting operation using machine learning method. Rock Mech Rock Eng (online published)

  18. Khandelwal M (2012) Prediction of safe charge to protect surrounding structures using support vector machine. Geotech Geol Eng (online published)

  19. Khandelwal M (2010) Evaluation and prediction of blast induced ground vibration using support vector machine. Int J Rock Mech Min Sci 47(3):509–516

    Article  Google Scholar 

  20. Khandelwal M, Singh TN (2009) Prediction of blast induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222

    Article  Google Scholar 

  21. Khandelwal M, Singh TN (2007) Evaluation of blast induced ground vibration predictors. Soil Dyn Earthq Eng 27(2):116–125

    Article  MathSciNet  Google Scholar 

  22. Konya CJ, Walter EJ (1991) Rock blasting and over break control. Federal Highway Administration, FHWA-HI-92-001

  23. Konya CJ, Walter EJ (2003) Rock blasting and over break control, 2nd edn. Federal Highway Administration, FHWA-HI-92-001

  24. Kulatilake PHSW, Qiong Wu, Hudaverdi T, Kuzu C (2010) Mean particle size prediction in rock blast fragmentation using neural networks. Eng Geol 114:298–311. doi:10.1016/j.enggeo.2010.05.008

    Article  Google Scholar 

  25. Lu Y (2005) Underground blast induced ground shock and its modeling using artificial neural network. Comput Geotech 32:164–178. doi:10.1016/j.compgeo.2005.01.007

    Article  Google Scholar 

  26. Mandal SK, Singh MM, Dasgupta S (2008) Theoretical concept to understand plan and design smooth blasting pattern. Geotech Geol Eng 26:399–416. doi:10.1007/s10706-008-9177-4

    Article  Google Scholar 

  27. Meulenkamp F, Alvarez Grima M (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36:29–39. doi:10.1016/S0148-9062(98)00173-9

    Article  Google Scholar 

  28. Mohamed MT (2009) Artificial neural network for prediction and control of blasting vibrations in Assiut (Egypt) limestone quarry. Int J Rock Mech Min Sci 46:426–431. doi:10.1016/j.ijrmms.2008.06.004

    Article  Google Scholar 

  29. Monjezi M, Bahrami A, YazdianVarjani A (2010) Simultaneous prediction of fragmentation and fly rock in blasting operation using artificial neural networks. Int J Rock Mech Min Sci 47:476–480. doi:10.1016/j.ijrmms.2009.09.008

    Article  Google Scholar 

  30. Monjezi M, Dehghani H (2008) Evaluation of effect of blasting pattern parameters on backbreak using neural networks. Int J Rock Mech Min Sci 45:1446–1453. doi:10.1016/j.ijrmms.2008.02.007

    Article  Google Scholar 

  31. Monjezi M, Ghafurikalajahia M, Bahrami A (2010) Prediction of blast-induced ground vibration using artificial neural networks. Tunn Undergr Sp Tech. doi:10.1016/j.tust.2010.05.002

    Google Scholar 

  32. Monjezi M, Rezaei M, Yazdian A (2010) Prediction of backbreak in open-pit blasting using fuzzy set theory. J Expert Syst Appl 37:2637–2643. doi:10.1016/j.eswa.2009.08.014

    Article  Google Scholar 

  33. Ozgen Karacan C (2008) Modeling and prediction of ventilation methane emissions of U.S. longwall mines using supervised artificial neural networks. Int J Coal Geol 73:371–387. doi:10.1016/j.coal.2007.09.003

    Article  Google Scholar 

  34. Santos OJ Jr, Celestino B (2008) Artificial neural networks analysis of Sao Paulo subway tunnel settlement data. Tunn Undergr Sp Tech 23:481–491. doi:10.1016/j.tust.2007.07.002

    Article  Google Scholar 

  35. Scoble MJ, Lizotte YC, Paventi M, Mohanty BB (1997) Measurement of blast damage. Min Eng 49:103–108

    Google Scholar 

  36. Stegemann JA, Buenfeld NR (2003) Prediction of unconfined compressive strength of cement paste containing industrial wastes. Waste Manag 23:321–332. doi:10.1016/S0956-053X(02)00062-4

    Article  Google Scholar 

  37. Singh SP (2000) The role of joints and rock mass quality in perimeter control. In: Proceedings of the 1st world conference on explosives and blasting technique, pp 371–375

  38. Tiryaki B (2008) Application of artificial neural networks for predicting the cuttability of rocks by drag tools. Tunn Undergr Sp Tech 2:273–280. doi:10.1016/j.tust.2007.04.008

    Article  Google Scholar 

  39. Levesque R (2007) SPSS programming and data management: a guide for SPSS and SAS users, 4th edn. SPSS, Chicago

    Google Scholar 

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Authors and Affiliations

  1. Department of Mining Engineering, Tarbiat Modares University, Tehran, Iran

    M. Monjezi & Z. Ahmadi

  2. Department of Electrical Engineering, Tarbiat Modares University, Tehran, Iran

    A. Yazdian Varjani

  3. Department of Mining Engineering, College of Technology and Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur, 313001, India

    Manoj Khandelwal

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  1. M. Monjezi
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  2. Z. Ahmadi
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  3. A. Yazdian Varjani
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  4. Manoj Khandelwal
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Correspondence to Manoj Khandelwal.

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Monjezi, M., Ahmadi, Z., Varjani, A.Y. et al. Backbreak prediction in the Chadormalu iron mine using artificial neural network. Neural Comput & Applic 23, 1101–1107 (2013). https://doi.org/10.1007/s00521-012-1038-7

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  • DOI: https://doi.org/10.1007/s00521-012-1038-7

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