Predicting the average size of blasted rocks in aggregate quarries using artificial neural networks

  • Lamprini Dimitraki
  • Basile Christaras
  • Vassilis Marinos
  • Ioannis Vlahavas
  • Nikolas Arampelos
Original Paper


The prediction of the average size of fragments in blasted rock piles produced after blasting in aggregate quarries is essential for decresing the cost of crushing and secondary breaking. There are several conventional and advanced processes to estimate the size of blasted rocks. Among these, the empirical prediction of the expected fragmentation in most cases is carried out by Kuznetsov’s equation (Sov Min Sci 9:144–148, 1973), modified by Lilly (1986) and Cunningham (1987). The present research focuses on the effect of the engineering geological factors and blasting process on the blasted fragments using a more powerful, advanced computational tool, an artificial neural network. In particular, the blast database consists of the blastability index of limestone on the pit face, the quantities of the explosives and of the blasted rock pile, assessing the interaction of these parameters on the blasted rocks. The data were collected from two aggregate quarries, Drymos and Tagarades, near Thessaloniki, in the Central Macedonia region of Greece. This approach indicates significant performance stability, providing the fragmentation size with high accuracy.


Artificial neural network Aggregate quarries Blastability index Blasted rock pile 



This research was carried out with scholarship by State Scholarships Foundation (IKY) that was funded in the framework of the “Scholarships for second cycle postgraduate courses” by the operational program “Human Resources Development, Education and Lifelong Learning”, 2014–2020 with the co-financing of the European Social Fund (ESF) and the Greek Public Investment.


  1. ASTM D 5873 (2000) Standard test methods for determination of rock hardness by rebound hammer method, Annual Book of ASTM Standards Soil and Rock, Building stones, Section-4, Construction, V.04.08, ASTM Publication, 972Google Scholar
  2. Bhandari S (1997) Engineering rock blasting operations. Balkema, RotterdamGoogle Scholar
  3. Christaras B, Chatziangelou M (2014) Blastability quality system (BQS) for using it, in bedrock excavation. Struct Eng Mech 51:823–845CrossRefGoogle Scholar
  4. Cunningham CVB (1987) Fragmentation estimations and the Kuz-Ram model – four years on. Proceedings of 1st International Symposium on Rock Fragmentation by Blasting, p 439–453Google Scholar
  5. Debes K, Koenig A, Gross HM (2005) Transfer functions in artificial neural networks. A simulation – based tutorial. Brains, Minds & Media.
  6. Diamantaras K (2007) Artificial neural networks. Kleidarithmos, Athens, GreeceGoogle Scholar
  7. Dimitraki L, Christaras V, Marinos V, Chatziangelou M (2016) The use of ultrasonic velocity for determining mechanical characteristics of limestones. Proceedings of XV Symposium Society of Geological Engineers and Technicians of Serbia, Belgrade, p 340–350Google Scholar
  8. Ermini L, Catani F, Casagli N (2005) Artificial neural networks applied to landside susceptibility assessment. Geomorphology 66:327–343CrossRefGoogle Scholar
  9. Fausett L (1994) Fundamentals of neural networks architectures algorithms and applications. Prentice Hall, New JerseyGoogle Scholar
  10. Ferentinou MD, Sakellariou MG (2007) Computational intelligence tools for the prediction of slope performance. Comput Geotech 34:362–384CrossRefGoogle Scholar
  11. Ge ZX, Sun ZQ (2007) Neural network theory and MATLAB R2007 application. Publishing House of Electronics Industry, BeijingGoogle Scholar
  12. Geological Map of the Chalkidiki Peninsula and adjacent areas (1976) Institute for Geological and Mining Research, AthensGoogle Scholar
  13. Gomez H, Kavzoglou T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River basin, Venezuela. Eng Geol 78:11–27CrossRefGoogle Scholar
  14. Graupe D (2007) Principles of artificial neural networks, 1st edn. University of Illinois, ChicagoCrossRefGoogle Scholar
  15. Hall J, Brunton I (2002) Critical comparison of Kruttschnitt mineral research center(JKMRC) blast fragmentation models. Fragblast 6(2):207–220CrossRefGoogle Scholar
  16. Haykin S (1999) Neural networks, a comprehensive foundation, 2nd edn. Prentice Hall, New JerseyGoogle Scholar
  17. Haykin S (2017) Neural networks and learning machines, 3rd edn. Pearson, LondonGoogle Scholar
  18. Heaton J (2008) Introduction to neural networks for java, 2nd edn. Heaton Research, USAGoogle Scholar
  19. Hecht-Nelson R (1987) Kolmogorov’s mapping neural network existence theorem. Proceedings of 1st IEEE Annual Int. Conf. on Neural Networks, San Diego, p 11–14Google Scholar
  20. Jain KY, Bhandare SK (2011) Min max normalization based data perturbation method for privacy protection. Int J Comput Commun Technol 2:45–50Google Scholar
  21. Jong YH, Lee CI (2004) Influence of geological conditions on the powder factor for tunnel blasting. Int J Rock Mech Min Sci 41(12):1–7Google Scholar
  22. Karami AR, Mansouri H, Farsangi MAE, Nezamabadi H (2006) Backbreak prediction due to bench blasting: an artificial neural network approach. J Mines Met Fuels 54:418–420Google Scholar
  23. Kulatilake PHSW, Qiong W, Hudaverdi T, Kuzu C (2010) Mean particle size prediction in rock blast fragmentation using neural networks. J Eng Geol 114:298–311CrossRefGoogle Scholar
  24. Kuznetsov VM (1973) The mean diameter of the fragments formed by blasting rock. Sov Min Sci 9:144–148CrossRefGoogle Scholar
  25. Latham JP, Lu P (1999) Development of an assessment system for the blastability of rock masses. Int J Rock Mech Min Sci 36:41–55CrossRefGoogle Scholar
  26. Lecun Y, Bottou L, Orr GB, Müller KR (1998) Efficient BackProp. In: Orr GB, Müller KR (eds) Neural networks: tricks of the trade. Lect Notes Comput Sci 1524:9–50Google Scholar
  27. Lee S, Ryu JH, Lee MJ, Won JS (2003) Use of an artificial neural network for analysis of the susceptibility to landslides at Boun, Korea. Environ Geol 44:820–833CrossRefGoogle Scholar
  28. Lilly P (1986) An empirical method of assessing rock mass blastability. AwIhfM/JTAust Large Open Pit. Mining Conference, Newman, p 89–92Google Scholar
  29. Lilly PA (1992) The use of the blastability index in the design of blasts for open pit mines. Western Australian Conference on Mining Geomechanics, Kalgoorlie, pp 421–426Google Scholar
  30. Lyana KN, Hareyani Z, Shah K, Hazizan MH (2016) Effect of geological condition on degree of fragmentation in a Simpang Pulai marble quarry. Procedia Chemistry 19:694–701CrossRefGoogle Scholar
  31. Marinos V, Marinos P, Hoek E (2005) The geological strength index: applications and limitations. Bull Eng Geol Environ 64:55–65CrossRefGoogle Scholar
  32. Mohanty B (1996) Rock fragmentation by blasting, 1st edn. CRC Press/Balkema, NetherlandsGoogle Scholar
  33. Monjezi M, Singh TN, Khandelwal M, Sinha S, Singh V, Hosseini I (2006) Prediction and analysis of blast parameters using artificial neural network. Noise Vibrat Worldwide 37:8–16CrossRefGoogle Scholar
  34. Neotectonic Map of Greece, Thessaloniki Sheet (1996) European Center of Prevention and Forecasting of Earthquakes, Earthquake Planning and Protection Organization Tectonic Committee of the Geological Society of Greece, AthensGoogle Scholar
  35. Nirgin A (1993) Neural networks for PatternRrecognition, 1st edn. MIT Press Cambridge, CambridgeGoogle Scholar
  36. Ratnesh T, Singh TN, Mudgal K, Gupta N (2014) Application of artificial neural network for blast performance evaluation. Int J Res Eng Technol 03:564–574Google Scholar
  37. Rezaei M, Monjezi M, Moghaddam SG, Farzaneh F (2012) Burden prediction in blasting operation using rock geomechanical properties. Arab J Geosci 5:1031–1037CrossRefGoogle Scholar
  38. Rosin P, Rammler E (1933) The laws governing the fineness of powdered coal. J Inst Fuel 7:29–36Google Scholar
  39. Rumelhart DE, McClelland JL, PDP Research Group (1986) Parallel distributed processing: explorations in the microstructure of cognition. MIT Press, CambridgeGoogle Scholar
  40. Tawadrous AS (2006) Evaluation of artificial neural networks as a reliable tool in blast design. Proceedings of the 32nd Annual Conference on Explosives and Blasting Techniques. International Society of Explosives Engineers, Dallas, JanuaryGoogle Scholar
  41. Verzani J (2014) Using R for introductory statistics, 2nd edn. CRC Press, FloridaGoogle Scholar
  42. Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79:251–266CrossRefGoogle Scholar
  43. Zurada J (1992) Introduction to artificial neural systems. West Group, EaganGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Lamprini Dimitraki
    • 1
  • Basile Christaras
    • 1
  • Vassilis Marinos
    • 1
  • Ioannis Vlahavas
    • 2
  • Nikolas Arampelos
    • 3
  1. 1.Department of GeologyAristotle University of ThessalonikiThessalonikiGreece
  2. 2.Department of InformaticsAristotle University of ThessalonikiThessalonikiGreece
  3. 3.ThessalonikiGreece

Personalised recommendations