Application of Artificial Neural Network (ANN) in the Determination of the Drillability Index (DI) of a Rock Mass

B. Besa; E.K. Chanda

doi:10.53974/unza.jonas.1.2.347

B. Besa Department of Mining Engineering, The University of Zambia, P.O. Box 32379, Lusaka
E.K. Chanda School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA 5005, Australia

DOI: https://doi.org/10.53974/unza.jonas.1.2.347

Keywords: Artificial Neural Networks, Drillability Index, Artificial Intelligence, Penetration Rate

Abstract

Artificial Neural Networks (ANN) have been applied to many interesting problems in different areas of science, medicine and engineering and in some cases, they provide state of-the-art solutions. This paper investigates the application of an ANN model in mining to predict the Drillability Index (DI) of a rock mass given rock parameters such as uniaxial compressive strength, shear strength, tensile strength, abrasion and hardness. Drillability indicates whether penetration is easy or hard while penetration rate indicates whether it is fast or slow. Therefore, prediction of the drillability and penetration rate is very important in rock drilling. Penetration rate is a necessary value for the cost estimation and the planning of the drilling project. According to results of this study, Uniaxial Compressive Strength (UCS) rating has the highest weight of 0.051083 among the three parameters studied which reconfirms the literature review finding which indicates that UCS is the most important parameter in predicting drillability.

References

1. Beste U., Jacobson S., Hogmark S. (2007).” Rock penetration into cemented carbide drill buttons during rock drilling”. Wear, 264: 1142-1151
2. Busuyi A.T. (2009).” Optimization of drilling and blasting operations in an open pit mine-the SOMAIR experience”. Min. Sci. Tech., 19(6): 736-739
3. Busuyi A.T. (2009).” Optimization of drilling and blasting operations in an open pit mine-the SOMAIR experience”. Min. Sci. Tech., 19(6): 736-739
4. Cogger, Kenneth O; Koch, Paul D; Lander, Diane M. (1997). “A Neural Network Approach to Forecasting Volatile International Equity Markets, Advances in financial economics. Volume 3”. Hirschey, Mark Marr, M. Wayne, eds., Greenwich, Conn. and London: JAI Press. p 117-57
5. Cooper, John C B., (1999). “Artificial Neural Networks versus Multivariate Statistics: An Application from Economics", Journal of Applied Statistics, Vol. 26 (8). p 909-21
6. DACS(1992). “Artificial Neural networks Technology” A State-of-the-Art Report, Data & Analysis Center for Software, Griffiss AFB, NY 13441-5700;
7. Garcia, R. and Gencay, R. (2000). "Pricing and Hedging Derivative Securities with Neural Networks and a Homogeneity Hint", Journal of Econometrics, Vol. 94 (1-2). p 93-115
8. Hamm, L and Brorsen, B. W. (2000). "Trading Futures Markets Based on Signals from a Neural Network", Applied Economics Letters, Vol. 7 (2). p 137-40.
9. Hawley, D. D., Johnson, J.D. and Raina, D. 13 (1990). “Artificial Neural Systems: A New Tool for Financial Decision-Making,” Financial Analysis Journal, 63-72.
10. Haykin, S., (1999).” Neural Networks – A Comprehensive Foundation. Prentice Hall, New Jersey”
11. Howarth, D. F. Adamson W. R. and Berdt, J. R. (1986). “Correlation of Model Tunnel Boring and Drilling Machine Performance with Rock Properties”, International Journal of Rock Mechanics and Mining Sciences.
12. Hu, Michael Y; Tsoukalas, C. (1999); "Combining Conditional Volatility Forecasts Using Neural Networks: An Application to the EMS Exchange Rates", Journal of International Financial Markets, Institutions & Money, Vol. 9 (4). p 407-22
13. Kapageridis I. (2002).”Artificial Neural Network Technology in Mining and Environmental Applications”. In: 11th International Symposium on Mine Planning and Equipment Selection (MPES 2002), VŠB - Technical University of Ostrava, Prague 2002
14. Matyas J. (1965). “Random Optimization”, Automation and remote control. Vol.26, pp 246-253.
15. McGregor K. (1967). “The drilling of rock”. London: C.R. Books Ltd.
16. Moshiri, S; Cameron, N. E; Scuse, D. (1999). “Static, Dynamic, and Hybrid Neural Networks in Forecasting Inflation”, Computational Economics, Vol. 14 (3). p 219-35.
17. Neural Ware Inc. (1991). “Neural Computing, Neural Works Professional II/PLUS, Neural Works Explore, Designer Pack, InstaNet, InstaProbe, and NeuralProbe, Trademarks of Neural Ware Inc Pittsburg, PA
18. Niyazi, B. (2011). “Determination of drillability of some natural stones and their association with rock properties”, Scientific Research and Essays, Vol. 6.
19. Rumelhart, D. and J. McClelland (1986). “Parallel Distributed Processing”. MIT Press, Cambridge, Mass
20. Rumelhart, D. and J. McClelland (1986). “Parallel Distributed Processing”. MIT Press, Cambridge, Mass
21. Shahin M. A., Jaksa M. B., Maier H.R., (2008).”State of the Art of Artificial Neural Networks in Geotechnical Engineering”, EJGE.
22. Shtub, A; Versano, R. (1999)."Estimating the Cost of Steel Pipe Bending, a Comparison between Neural Networks and Regression Analysis", International Journal of Production Economics, Vol. 62 (3). p 201-07
23. Solis, F. J. and Wets, R.J.B. (1981). “Minimization by Random Search Techniques”, Mathematics of Operations Research, Vol. 6, No.1, pp 19-30.
24. Terna, P. (1997). "Neural Network for Economic and Financial Modelling: Summing Up Ideas Emerging from Agent Based Simulation and Introducing an Artificial Laboratory", Cognitive Economics, Viale, Riccardo, ed., LaSCoMES Series, vol. 1. Torino: La Rosa. p 271-309.
25. White, H., (1988). “Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns,” Proceedings of the IEEE International Conference of Neural Networks, July 1988, II451-II458.
26. White, H., (1996). “Option Pricing in Modern Finance Theory and the Relevance of Artificial Neural Networks,” Discussion Paper, Econometrics Workshop, March 1996.
27. Yu, J., (2004). “Artificial Neural Network” Specialized advanced studies, Master in Artificial Inteligence, Faculteit Toegepast Wetenschappen, Katholieke University Leuven