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T Sasaoka H Shimada T Sasaki M Ichinose K Matsui 2008 Study on control of rock fragmentation at limestone quarry J Coal Sci Eng 14 365 368 GF Brent GE Smith GN Lye 2002 Studies on the effect of burden on blast damage and the implementation of new blasting practices to improve productivity at KCGMs Fimiston Mine Fragblast 6 189 206 JA Sanchidrian P Segarra LM Lopez 2007 Energy components in rock blasting Int J Rock Mech Min Sci 44 130 147 Gengshe Y, Dingyi X, Changqing Z, Yibin P (1999) CT analysis on mechanic characteristics of damage propagation of rock. TR Yu S Vongpaisal 1996 New blast damage criteria for underground blasting CIM Bull 89 139 145Įloranta J (2003) Characterization of the pre and post blast environments. Mohanty (Ed), Rock Fragm by Blasting 131–8 Paventi M, Lizotte Y, Scoble M, Mohanty B (1996) Measuring rock mass damage in drifting. Persson P-A, Holmberg R, Lee J (1993) Rock blasting and explosives engineering.

M Monjezi M Rezaei A Yazdian 2010 Prediction of backbreak in open-pit blasting using fuzzy set theory Expert Syst A2643 J Liaoning Tech Univ Nat Sci 12:16ĭ Longjun L Xibing X Ming L Qiyue 2011 Comparisons of random forest and support vector machine for predicting blasting vibration characteristic parameters Procedia 1781ĬJ Chen CH Liu YJ Chen YJ Shen 2016 Evaluation of machine learning methods for ground vibration prediction model induced by high-speed railway J Vib Eng Technol 4 283 290

Li H, Feng D, Ma H (2015) Random forest prediction model and its application to predicting house hazard from cutting excavation blasting. L Breiman 2001 Random forests Mach Learn 45 5 32 TG Dietterich 1998 An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization Mach Learn 32 1 22 H Sheykhi R Bagherpour E Ghasemi H Kalhori 2018 Forecasting ground vibration due to rock blasting: a hybrid intelligent approach using support vector regression and fuzzy C-means clustering Eng Comput 34 357 365 M Monjezi M Ahmadi M Sheikhan A Bahrami AR Salimi 2010 Predicting blast-induced ground vibration using various types of neural networks Soil Dyn Earthq 1236 Eng Comput pp 1–7Ī Saghatforoush M Monjezi RS Faradonbeh DJ Armaghani 2016 Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting Eng Comput 32 255 266 Tian E, Zhang J, Tehrani MS, Surendar A, Ibatova AZ (2018) Development of GA-based models for simulating the ground vibration in mine blasting. M Monjezi H Dehghani 2008 Evaluation of effect of blasting pattern parameters on back break using neural networks Int J Rock Mech Min 1453 Rock Mech., American Rock Mechanics Association Gates WCB, Ortiz LT, Florez RM (2005) Analysis of rockfall and blasting backbreak problems, US 550, Molas Pass, CO. Konya CJ (2003) Blast design in rock blasting and overbreak control The same results were validated using Random forest method wherein the model R 2 was 0.9791 and RMSE was 0.8799.ĬL Jimeno EL Jimeno FJA Carcedo YV Ramiro De 1995 Drilling and blasting of rocks, geomining technological institute of Spain Rotterdam Netherlands AA Balkema From the field trials, it was evident that the use of low-density emulsion can help in reducing the back break and optimise the overall cost of the blasting process. For the random forest model, R 2 0.9791 and RMSE 0.87899 and for linear regression was R 2 was 0.824 and root mean square error (RMSE) 0.72, respectively. The variables used for the study was such as burden to spacing ratio, stemming to hole-depth ratio, p-wave velocity and the density of explosive. In this paper, an attempt is made to predict back break using the random forest method. Therefore, predicting and subsequently optimising back break shall reduce their problems to some extent.

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Due to improper free face created from the previous blast and the presence of loose strata in the face increases the overall cost of production. It can cause rockfall during drilling due to the cracks present in the in situ rock mass at the perimeter. It does not help in creating a smooth high wall and free face for next blasting due to cracks, overhang and under-hang. Back break is an unsolicited phenomenon caused due to rock condition, blast geometry, explosive and initiation system in mines.