Minimising Backbreak at the Dewan Cement Limestone Quarry Using an Artificial Neural Network

Open access


Backbreak, defined as excessive breakage behind the last row of blastholes in blasting operations at a quarry, causes destabilisation of rock slopes, improper fragmentation, minimises drilling efficiency. In this paper an artificial neural network (ANN) is applied to predict backbreak, using 12 input parameters representing various controllable factors, such as the characteristics of explosives and geometrical blast design, at the Dewan Cement limestone quarry in Hattar, Pakistan. This ANN was trained with several model architectures. The 12-2-1 ANN model was selected as the simplest model yielding the best result, with a reported correlation coefficient of 0.98 and 0.97 in the training and validation phases, respectively. Sensitivity analysis of the model suggested that backbreak can be reduced most effectively by reducing powder factor, blasthole inclination, and burden. Field tests were subsequently carried out in which these sensitive parameters were varied accordingly; as a result, backbreak was controlled and reduced from 8 m to less than a metre. The resulting reduction in powder factor (kg of explosives used per m3 of blasted material) also reduced blasting costs.


  • Ashby J.P., 1981. Production blasting and development of open pit slopes. [In] Production blasting and development of open pit slopes.

  • Bhalchandra V.G., 2011. Rotary Drilling and Blasting in Large Surface Mines.

  • Bonaventura X., Sima A.A., Feixas M., Buckley S.J., Sbert M., Howell J.A., 2017. Information measures for terrain visualization. Computers and Geosciences, 99 (October 2016), 9-18.

  • Chatterjee S., Bandopadhyay S., Machuca D., 2010. Ore grade prediction using a genetic algorithm and clustering based ensemble neural network model. Mathematical Geosciences, 42 (3), 309-326.

  • Cherkassky V., Krasnopolsky V., Solomatine D.P., Valdes J., 2006. Computational intelligence in earth sciences and environmental applications: Issues and challenges. Neural Networks, 19 (2), 113-121.

  • Demicco R.V., Klir G.J., 2004. Fuzzy logic in geology. Elsevier Academic Press.

  • Ebrahimi E., Monjezi M., Khalesi M.R., Armaghani D.J., 2016. Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bulletin of Engineering Geology and the Environment, 75 (1), 27-36.

  • Faramarzi F., Ebrahimi Farsangi M.A., Mansouri H., 2012. An RES-Based Model for Risk Assessment and Prediction of Backbreak in Bench Blasting. Rock Mechanics and Rock Engineering, 46(4), 877-887.

  • Gates W.C.B., Ortiz L.T., Florez R.M., 2005. Analysis of Rockfall and Blasting Backbreak Problems. US 550, Molas Pass, Colorado, USA.

  • Ghasemi E., Amnieh H. B., Bagherpour R., 2016. Assessment of backbreak due to blasting operation in open pit mines: a case study. Environmental Earth Sciences, 75 (7), 1-11.

  • Hopfield J.J., Tank D.W., 1984. “Neural” computation of decisions in optimization problems. Biological Cybernetics, 52.

  • Izadi H., Sadri J., Bayati M., 2017. An intelligent system for mineral identification in thin sections based on a cascade approach. Computers and Geosciences, 99 (November 2015), 37-49.

  • Jang H., Topal E., 2013. Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network. Tunnelling and Underground Space Technology, 38, 161-169.

  • Kar S., Das S., Ghosh P.K., 2014. Applications of neuro fuzzy systems: A brief review and future outline. Applied Soft Computing, 15, 243-259.

  • Khandelwal M., Monjezi M., 2012. Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method. Rock Mechanics and Rock Engineering, 46 (2), 389-396.

  • Khandelwal M., Roy M., Singh M.P., Singk P.K., 2004. Application of artificial neural network in mining industry. India Mining Engineering Journal, 43, 19-23.

  • Konya C.J., Walter E.J., 1991. Rock Blasting and Overbreak Control.

  • Mehrotra K., Mohan C.K., Ranka S., 1997. Elements of Artificial Neural Networks. Cambridge, MA, USA: MIT Press.

  • Mohammadnejad M., Gholami R., Sereshki F., Jamshidi, A., 2013. A new methodology to predict backbreak in blasting operation. International Journal of Rock Mechanics and Mining Sciences, 60, 75-81.

  • Monjezi M., Bahrami A., Yazdian Varjani A., 2010. Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. International Journal of Rock Mechanics and Mining Sciences, 47 (3), 476-480.

  • Monjezi M., Dehghani H., 2008. Evaluation of effect of blasting pattern parameters on back break using neural networks. International Journal of Rock Mechanics and Mining Sciences, 45 (8), 1446-1453.

  • Monjezi M., Hashemi Rizi S. M., Majd V. J., Khandelwal M., 2014. Artificial Neural Network as a Tool for Backbreak Prediction. Geotechnical and Geological Engineering, 32 (1), 21-30.

  • Monjezi M., Rezaei M., Yazdian A., 2010. Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Systems with Applications, 37 (3), 2637-2643.

  • Muhammad K., Glass H.J., 2011. Modelling Short-Scale Variability and Uncertainty During Mineral Resource Estimation Using a Novel Fuzzy Estimation Technique. Geostandards and Geoanalytical Research, 35 (3), 369-385.

  • Muhammad K., Mohammad N., Rehman F., 2015. Modeling Shotcrete Mix Design using Artificial Neural Network. Computers and Concrete, 15 (2), 167-181.

  • Olofsson S.O., 1990. Applied explosives technology for construction and mining. APPLEX. Retrieved from

  • Oraee K., Asi B., 2006. Prediction of Rock Fragmentation in Open Pit Mines, using Neural Network Analysis. Fifteenth International Symposium on Mine Planning and Equipment Selection (MPES 2006), Turin, Italy.

  • Rogiers B., Mallants D., Batelaan O., Gedeon M., Huysmans M., Dassargues A., 2012. Estimation of Hydraulic Conductivity and Its Uncertainty from Grain-Size Data Using GLUE and Artificial Neural Networks. Mathematical Geosciences, 44 (6), 739-763.

  • Roslin A., Esterle J.S. 2016. Electrofacies analysis for coal lithotype profiling based on high-resolution wireline log data. Computers and Geosciences, 91, 1-10.

  • Saadat M., Khandelwal M., Monjezi M., 2014. An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran. Journal of Rock Mechanics and Geotechnical Engineering, 6 (1), 67-76.

  • Sari M., Ghasemi E., Ataei M., 2013. Stochastic Modeling Approach for the Evaluation of Backbreak due to Blasting Operations in Open Pit Mines. Rock Mechanics and Rock Engineering, 47 (2), 771-783.

  • Sayadi A., Monjezi M., Talebi N., Khandelwal M., 2013. A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak. Journal of Rock Mechanics and Geotechnical Engineering, 5 (4), 318-324.

  • Smith M.R., Collis L., Fookes P.G., 2001. Aggregates: Sand, Gravel and Crushed Rock Aggregates for Construction Purposes. Geological Society. Retrieved from

  • Tadeusiewicz R., 2015. Neural Networks In Mining Sciences ‒ General Overview And Some Representative Examples. Archives of Mining Sciences, 60 (4), 971-984.

  • Valdés J.J., Bonham-Carter G., 2006. Time dependent neural network models for detecting changes of state in complex processes: Applications in earth sciences and astronomy. Neural Networks, 19 (2), 196-207.

  • Workman L., 1992. Wall Control. Retrieved from

  • Wyllie D.C., Mah C., 2004. Rock Slope Engineering, Fourth Edition: Fourth edition. Taylor & Francis. Retrieved from

  • Yegireddi S., Uday Bhaskar G., 2009. Identification of coal seam strata from geophysical logs of borehole using Adaptive Neuro-Fuzzy Inference System. Journal of Applied Geophysics, 67 (1), 9-13.

  • Yurdakul M., Gopalakrishnan K., Akdas H., 2014. Prediction of specific cutting energy in natural stone cutting processes using the neuro-fuzzy methodology. International Journal of Rock Mechanics and Mining Sciences, 67, 127-135.

  • Zoveidavianpoor M., Samsuri A., Shadizadeh S. R., 2013. Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir. Journal of Applied Geophysics, 89, 96-107.

Archives of Mining Sciences

The Journal of Committee of Mining of Polish Academy of Sciences

Journal Information

IMPACT FACTOR 2016: 0.550
5-year IMPACT FACTOR: 0.610

CiteScore 2016: 0.72

SCImago Journal Rank (SJR) 2016: 0.320
Source Normalized Impact per Paper (SNIP) 2016: 0.950


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 16 16 16
PDF Downloads 5 5 5