Abstract
Backbreak is an undesirable phenomenon in blasting operations. It can cause instability of mine walls, falling down of machinery, improper fragmentation, reduced efficiency of drilling, etc. The existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, the application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict backbreak in blasting operations of Soungun iron mine, Iran, incorporating rock properties and blast design parameters using the support vector machine (SVM) method. To investigate the suitability of this approach, the predictions by SVM have been compared with multivariate regression analysis (MVRA). The coefficient of determination (CoD) and the mean absolute error (MAE) were taken as performance measures. It was found that the CoD between measured and predicted backbreak was 0.987 and 0.89 by SVM and MVRA, respectively, whereas the MAE was 0.29 and 1.07 by SVM and MVRA, respectively.
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Abbreviations
- SVM:
-
Support vector machine
- MVRA:
-
Multivariate regression analysis
- CoD:
-
Coefficient of determination
- MAE:
-
Mean absolute error
- AI:
-
Artificial intelligence
- ANN:
-
Artificial neural network
- VC:
-
Vapnik–Chervonenkis
- L:
-
Hole length (m)
- S:
-
Spacing (m)
- B:
-
Burden (m)
- T:
-
Stemming (m)
- PF:
-
Powder factor (kg/ton)
- SD:
-
Specific drilling (m/m3)
- BB:
-
Backbreak (m)
- SMO:
-
Sequential minimal optimization
- w :
-
N-dimensional vector
- b :
-
Scalar value
- λ i :
-
Lagrange multipliers
- ξ i :
-
Slack variables
- C :
-
Error penalty
References
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, Cambridge
Feng X-T, Zhao H, Li S (2004) Modeling non-linear displacement time series of geo-materials using evolutionary support vector machines. Int J Rock Mech Min Sci 41(7):1087–1107
Gate WC, Ortiz BLT, Florez RM (2005) Analysis of rockfall and blasting backbreak problems. In: Proceedings of the American rock mechanics conference, Paper ARMA/USRMS 05-671, vol 5, p 671–680
Jenkins SS (1981) Adjusting blast design for best results. Pit and quarry. Balkema, Rotterdam
Jimeno CL, Jimeno EL, Carcedo FJA (1995) Drilling and blasting of rocks. Balkema, Rotterdam
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
Khandelwal M (2011) Blast-induced ground vibration prediction using support vector machine. Eng Comput 27(3):193–200. doi:10.1007/s00366-010-0190-x
Khandelwal M, Kankar PK (2011) Prediction of blast-induced air overpressure using support vector machine. Arab J Geosci 4(3–4):427–433. doi:10.1007/s12517-009-0092-7
Khandelwal M, Singh TN (2006) Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach. J Sound Vib 289(4–5):711–725
Khandelwal M, Singh TN (2007) Evaluation of blast-induced ground vibration predictors. Soil Dyn Earthq Eng 27(2):116–125
Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46(7):1214–1222
Konya CJ (2003) Rock blasting and overbreak control, 2nd edn. National Highway Institute, USA (FHWA-HI-92-001)
Konya CJ, Walter EJ (1991) Rock blasting and overbreak control, 1st edn. National Highway Institute, USA (FHWA-HI-92-001)
Liu KY, Qiao CS, Tian SF (2004) Design of tunnel shotcrete-bolting support based on a support vector machine approach. Int J Rock Mech Min Sci 41(3):510–511
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
Monjezi M, Rezaei M, Yazdian A (2010a) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Syst Appl 37(3):2637–2643
Monjezi M, Bahrami A, Yazdian Varjani A (2010b) Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int J Rock Mech Min Sci 47(3):476–480
Muller KR, Smola JA, Scholkopf B (1997) Prediction time series with support vector machines. In: Proceedings of the 7th international conference on artificial neural networks (ICANN), Lausanne, Switzerland, October 1997, pp 999–1004
Platt JC (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. Technical Report, MSR-TR-98-14
Schmidt M (1996) Identifying speaker with support vector networks. In: Proceedings of the 28th symposium on the interface (Interface’96), Sydney, Australia, July 1996
Scholkopf B, Burges C, Vapnik V (1995) Extracting support data for a given task. In: Proceedings of the international conference on knowledge discovery and data mining, Menlo Park, CA, USA, August 1995, AAAI Press
Vapnik VN (1998) Statistical learning theory. Wiley, New York
Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco
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Khandelwal, M., Monjezi, M. Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method. Rock Mech Rock Eng 46, 389–396 (2013). https://doi.org/10.1007/s00603-012-0269-3
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DOI: https://doi.org/10.1007/s00603-012-0269-3