Document Type : Original Research Paper


Department of Mining Engineering, Federal University of Technology, Akure, Nigeria


Assessment of blast results is a significant approach for the improvement of mining operations. The different procedures for investigating rock fragmentation have their limitations, causing different variation prediction errors. Thus every technique is site-explicit, and applicable for a few explicit purposes. This work evaluates the existing empirical blast fragmentation model predictions in the case study of small-scale dolomite quarries. An attempt is made to compare the prediction accuracy of the modified Kuz-Ram model, Lawal 2021 model, and Kuznetsov-Cunningham-Ouchterlony (KCO) model with the WipFrag© analysis result and proposed artificial neural network (ANN) models. The prediction error analysis of the current models and that of the new proposed ANN models is evaluated using the three model assessment indices. The assessment indices uncover that the KCO model when compared to the modified Kuz-Ram model has the least error for most blast round percentage passing size predicted. However, the proposed artificial neural network models show high prediction exactness in predicting blast fragment mean size than the existing empirical models. Therefore, the proposed ANN models can be used to improve the productivity of small-scale dolomite blasting operation results for practical purposes.


[1]. Steven, J. (2013). Ergonomic Hazards Associated with Small Scale Mining in Southern
Africa, International Journal of pure and applied Sciences and technology. 15 (2): pp. 8-17.
[2]. Jug, J. (2017). Fragment size distribution of blasted rock mass, IOP Conference Series, Earth and Environmental Science. 95 (4): pp.1–9.
[3]. Kulatilake, P.H.S.W., Wu, Q., Hudaverdi, T. and Kuzu, C. (2010). Mean particle size prediction in rock blast fragmentation using neural networks, Engineering Geology. 114 (3-4): pp. 298-311.
[4]. Dinis, C. and Da Gamaand Lopez Jimeno, C. (1993). Rock fragmentation control for blasting cost minimization and environmental impact a battement, Procs. 4th International Symposium on Rock Fragmentation by Blasting (Fragblast-4).Vienna, Austria, pp. 273-280.
[5]. Workman, L. and Eloranta, J. (2003). The effects of blasting on crushing and grinding efficiency and energy consumption, In: Proc 29th Con Explosives and Blasting Techniques, Int Society of Explosive Engineers, Cleveland OH. pp. 1–5.
[6]. Gheibie, S.,Aghababaeia, H.,Hoseinieb, S.S.H. and Pourrahimianc, Y. (2009). Modified Kuz
Ram fragmentation model and its use at the Sungun Copper Mine, International Journal of Rock Mechanics and Mining Sciences. 46 (6): pp. 967–973.
[7]. Moray, S. (2006). Energy efficiency opportunities in the stone and asphalt industr, In:
Proceedings of the Twenty-Eighth Industrial Energy Technology Conference, New Orleans, LA. pp.71–83.
[8]. Mutinda, E.K.,Alunda, B.O.,Maina, D.K. and Kasomo, R.M. (2021). Prediction of rock fragmentation using the Kuznetsov-Cunningham-Ouchterlony model, The Journal of the Southern African Institute of Mining and Metallurgy, 121p.
[9]. Tiile, R.N. (2016). Artificial neural network approach to predict blast-induced  groundvibration,
Airblast and rock Fragmentation. Masters Theses. Missouri university ofscience and Technology, Faculty of the Graduate School, Department of Mining Engineering: Missouri. 7571 p.
[10]. Petrosyan, M.I. (2018). Model investigations of parameters of rock breakage by blasting.
Rock Breakage by Blasting, Routledge. pp. 75–104.
[11]. Lawal, A.I. (2021). A new modification to the Kuz-Ram model using the fragment size predicted by image analysis, International Journal of Rock Mechanics and Mining. doi:10.1016/j.ijrmms.2020.104595.
[12]. Babaeian, M., Ataei, M., Sereshki, F., Sotoudeh, F. and Mohammadi, S. (2019). A new framework for evaluation of rock fragmentation in open pit mines. Journal of Rock Mechanics and Geotechnical Engineering. 11 (2): pp. 325–336.
[13]. Ouchterlony, F. and Sanchidrián, J.A. (2019). A review of development of better prediction equations for blast fragmentation, Journal of Rock Mechanics and Geotechnical Engineering. 11 (5): pp.1094–1109.
[14]. Franklin, J.A., Kemeny, J.M. and Girdner, K.K. (1986).  Evolution of system: A Review Proceedings of the FRAGBLAST 5 Workshop on Measurement of Blast Fragmentation, Montreal, Quebec, Canada, pp. 47-52.
[15]. Da Gamma, C.D. (1983). Use of Comminution Theory to Predict Fragmentation of Jointed
Rock Masses Subjected to Blasting. In: Proceedings of the First International Symposium on Rock Fragmentation by Blasting, Lulea, Sweden, pp. 565–579.
[16]. Cunningham, C.V.B. (2005). The Kuz-Ram fragmentation model–20 years on, Proceedings of the 3rd European Federation of Explosives Engineers World Conference on Explosives and Blasting, Brighton. 4, pp. 201–210.
[17]. Abuhasel, K.A. (2019). A comparative study of regression model and the adaptive neuro-fuzzy conjecture systems for predicting energy consumption for jaw crusher, Applied Sciences. 9 (18): pp. 3916.
[18]. Sayadi, A.,Monjezi, M., Talebi, N. and 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): pp.318-324.
[19]. Cardu, M.,Coragliotto, D. and Oreste, P. (2019). Analysis of predictor equations for determining the blast-induced vibration in rock blasting, International Journal of Mining Science and Technology. 29 (6): 905pp.
[20]. Da Gamma, C.D. (1984). Microcomputer simulation of rock blasting to predictfragmentation, Proceedings of the 25th U.S. Symposium on Rock Mechanics,Evanston, Illinois, pp. 1018-1030.
[21]. Larsson, B., Hemgren, W., and Brohn, C.E. (1973). Styckefallsutredning. Skanska, Cementgjuteriet.
[22]. Kuznetsov, V.N. (1973). The Mean Diameter of the Fragments Formed by Blasting of Rock. Soviet Mining Sci.Part 2, pp. 39–43.
[23]. Cunningham, C.V.B. (1983). The Kuz-Ram model for prediction of fragmentation from blasting. In: Proceedings of the 1st international symposium on rock fragmentation by blasting. Sweden: Luleå University of Technology. P. 439-453.
[24]. Lilly, PA. (1986). An empirical method of assessing rock mass blastability. In: Proceedings of the large open pit mine conference. Carlton, Australia: AusIMM. pp. 89-92.
[25]. Cunningham, C.V.B. (1987). Fragmentation estimations and the Kuz-Ram model e four years on. In: Proceedings of the 2nd international symposium on rock fragmentation by blasting. p. 475-487.
[26]. Chung, S.H. and Katsabanis, P.D. (2000). Fragmentation prediction using improved engineering formulae. Fragblast. 4 (3): pp. 198–207.
[27]. Sanchidrián, J.A., Segarra, P. and López, L.M. (2006). A practical procedure for the measurement of fragmentation by blasting by image analysis. Rock Mechanics and Rock Engineering. 39 (4): pp. 359–382.
[28]. Otterness, R.E., Stagg, M.S., Rholl, S.A. and Smith, N.S. (1991). Correlation of shot design parameters to fragmentation. In: Proceedings of the 7th annual symposium on explosives and blasting Technology. International Society of Explosives Engineers (ISEE), pp. 90-179.
[29]. Stagg, M.S., Rholl, S.A., Otterness, R.E. and Smith, N.S. (1990). Influence of shot design parameters on fragmentation. In: Proceedings of the 3rd international symposium on rock fragmentation by blasting. Carlton, Australia: AusIMM. p. 7-311.
[30]. Djordjevic, N. (1999). Two-componentmodel of blast fragmentation. In: Proceedings of the 6th international symposium on rock fragmentation by blasting, Johannesburg: The Southern African Institute of Mining and Metallurgy (SAIMM). p. 9-213.
[31]. Kanchibotla, S.S., Valery, W. and Morell, S. (1999). Modelling fines in blast fragmentation and its impact on crushing and grinding. In: Proceedings of the explosive, Carlton, Australia: AusIMM. p. 44-137.
[32]. Thornton, D.M., Kanchibotla, S.S. and Esterle, J.S. (2001). A fragmentation model to estimate
ROM size distribution of soft rock types, In Proceedings of the 27th annual conference on explosives and blasting Technology. ISEE. pp. 41-53.
[33]. Esen, S., Onederra, I. and Bilgin, H.A. (2003). Modelling the size of the crushed zone around a blasthole. International Journal of Rock Mechanics and Mining Sciences. 40 (4): pp. 485-495.
[34]. Onederra, I., Esen, S. and Jankovic, A. (2004). Estimation of fines generated by blasting applications for the mining and quarrying industries. Mining Technology. 113 (4): pp.237-247.
[35]. aerz, N.H. (1996). Image Sampling Techniques and Requirements for Automated Image Analysis of Rock Fragments, In: Proceedings of ISRM/Fragblast 5 Workshop and Short Course on Fragmentation Measurement . Montreal, A.A. Balkema.
[36]. Bieniawski, Z.T. (1989). Engineering Rock Mass Classifications. In A Complete Manual for Engineers and Geologists in Mining, Civil and Petroleum Engineering, Toronto: John Wiley & Sons.
[37]. ISRM (1981). Rock characterization, testing and monitoring. In: Brown, E.T. (Ed.). ISRM suggested methods. Commission on Testing Methods, International Society for Rock Mechanics (ISRM), Pergam on Press, Oxford, UK.pp. 75-105.
[38]. ISRM (2007). The ISRM suggested method for rock characterization, testing and monitoring: Eds.R. Ulusay and J.A Judson, IRSM. pp. 1974-2006.
[39]. Sanchidrián, J.A.and Ouchterlony, F. (2017). A distribution-free description of fragmentation by blasting based on dimensional analysis, Rock Mechanics and Rock Engineering, 50(4), pp. 781–806.
[40]. Faramarz, F.,Mansouri, H. and Ebrahimi, F.M.A. (2013). A Rock Engineering System Based Model to Predict Rock Fragmentation by Blasting, International Journal of Rock Mechanics and Mining Science, 60, pp. 82-94.
[41]. Tosun, A., Konak, G., Toprak, T., Karakus, D. and Onur, A.H. (2014). Development of the Kuz-Ram model to blasting in a limestone quarry. Arch Min Science. 59 (2): pp. 477–488.
[42]. Shad, H.I.A., Sereshki, F., Ataei, M., and Karamoozian, M. (2018).  Investigation of rock blast fragmentation based on specific explosive energy and in-situ block size. International Journal of Mining and Geological Engineering, pp.52–1:1–6.