Abstract
The present paper mainly deals with the prediction of maximum explosive charge used per delay (Q MAX) using an artificial neural network (ANN) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). One hundred and fifty blast vibration data sets were monitored at different vulnerable and strategic locations in and around major coal producing opencast coal mines in India. One hundred and twenty-four blast vibrations records were used for the training of the ANN model vis-à-vis to determine site constants of various conventional vibration predictors. The other 26 new randomly selected data sets were used to test, evaluate and compare the ANN prediction results with widely used conventional predictors. Results were compared based on coefficient of correlation (R), mean absolute error and mean squared between measured and predicted values of Q MAX. It was found that coefficient of correlation between measured and predicted Q MAX by ANN was 0.985, whereas it ranged from 0.316 to 0.762 by different conventional predictor equations. Mean absolute error and mean squared error was also very small by ANN, whereas it was very high for different conventional predictor equations.
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References
Abbassi A, Bahar L (2005) Application of neural network for the modeling and control of evaporative condenser cooling load. Appl Therm Eng 25:3176–3186
Alipour A, Ashtiani M (2011) Fuzzy modeling approaches for the prediction of maximum charge per delay in surface mining. Int J Rock Mech Min Sci 48:305–310
Ambraseys NR, Hendron AJ (1968) Dynamic behaviour of rock masses, rock mechanics in engineering practices. Wiley, London, pp 203–207
Baheer I (2000) Selection of methodology for modeling hysteresis behavior of soils using neural networks. J Comput Aided Civil Infrastruct Eng 5(6):445–463
Bureau of Indian Standard. Criteria for safety and design of structures subjected to underground blast. ISI Bulletin 1973; IS-6922
Chauvin Y, Rumelhart DE (1995) Backpropagation: theory, architectures and applications. Erlbaum, Mahwah, p 561. ISBN: 080581258X
Cheng G, Huang SL. Analysis of ground vibration caused by open pit production blast. In: Holmberg (ed) Explosive and blasting technique. Balkema; 2000, pp 63–70
Davies B, Farmer IW, Attewell PB (1964) Ground vibrations from shallow sub-surface blasts, vol 217. The Engineer, London; 1964, pp 553–559
Dowding CH (1985) Blast vibration monitoring and control. Prentice-Hall Inc, Englewoods Cliffs, pp 288–290
Duvall WI, Petkof B (1959) Spherical propagation of explosion generated strain pulses in rock. USBM, RI 5483, p 21
Hagan TN (1973) Rock breakage by explosives. In: Proceedings of the national symposium on rock fragmentation, Adelaide, pp 1–17
Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the first IEEE international conference on neural networks, San Diego, pp 11–14
Hino K (1956) Fragmentation of rock through blasting. J Ind Explosive Soc 17(1):1–11
Hush DR (1989) Classification with neural networks: a performance analysis. In: Proceedings of the IEEE international conference on systems engineering, Dayton, pp 277–280
ISRM (1992) Suggested method for blast vibration monitoring. Int J Rock Mech Min Sci Geomech Abst 29:145–146
Kanellopoulas I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sensing 18:711–725
Khandelwal M, Singh TN (2006) Prediction of blast induced ground vibrations and frequency in opencast mine—a neural network approach. J Sound Vib 289:711–725
Khandelwal M, Singh TN (2007) Evaluation of blast induced ground vibration predictors. Soil Dyn Earthq Eng 27:116–125
Khandelwal M, Singh TN (2009) Prediction of blast induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222
Kosko B (1994) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice-Hall, New Delhi, pp 12–17
Langefors U, Kihlstrom B (1963) The modern technique of rock blasting. Wiley, New York
Lippmann RP (1987) An introduction to computing with neural nets. IEEE ASSP Mag 4:4–22
MacKay DJC (1992) Bayesian interpolation. Neural Comput 4:415–447
Masters T (1994) Practical neural network recipes in C++. Academic Press, Boston
McKenzie C (1990) Quarry blast monitoring technical and environmental perspective. Quarry Manag, 23–29
Neaupanea KM, Achet SH (2004) Use of backpropagation neural network for landslide monitoring, a case study in the higher Himalaya. J Eng Geol 74:213–226
Pal Roy P (1993) Putting ground vibration predictors into practice. Colliery Guard 241:63–67
Peisheng L, Youhui X, Dunxi Y, Xuexin S (2005) Prediction of grindability with multivariable regression and neural network in Chinese coal. Fuel 84:2384–2388
Rai R, Shrivastva BK, Singh TN (2005) Prediction of maximum safe charge per delay in surface mining. Trans Inst Min Metall A 114(A):227–231
Ripley BD (1993) Statistical aspects of neural networks. In: Barndoff-Neilsen OE, Jensen JL, Kendall WS (eds) Networks and chaos-statistical and probabilistic aspects. Chapman & Hall, London, pp 40–123
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representation by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing, vol 1, pp 318–362
Simpson PK (1990) Artificial neural system; foundation, paradigm, application and implementations. Pergamon, New York
Tampe SS, Kulkarani BDD, Deshpande PB (1996) Elements of artificial neural networks with selected applications in chemical engineering, and chemical and biological sciences, simulation and advanced controls, Louisville
Trippi RR, Turban E (1996) Neural networks in finance and investing-using artificial intelligence to improve real-world performance, revised ed. McGraw-Hill, New York
Wang C (1994) A theory of generalization in learning machines with neural application. PhD thesis, The University of Pennsylvania, USA
Wasserman PD (1989) Neural computing theory and practice. Van Nostrand Reinhold, New York
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Khandelwal, M., Singh, T.N. Application of an Expert System to Predict Maximum Explosive Charge Used Per Delay in Surface Mining. Rock Mech Rock Eng 46, 1551–1558 (2013). https://doi.org/10.1007/s00603-013-0368-9
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DOI: https://doi.org/10.1007/s00603-013-0368-9