SciELO - Scientific Electronic Library Online

 
vol.110 número8Efficiency analysis of armed-chained cutting machines in block production in travertine quarries índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Artigo

Indicadores

Links relacionados

  • Em processo de indexaçãoCitado por Google
  • Em processo de indexaçãoSimilares em Google

Compartilhar


Journal of the Southern African Institute of Mining and Metallurgy

versão On-line ISSN 2411-9717

J. S. Afr. Inst. Min. Metall. vol.110 no.8 Johannesburg Ago. 2010

 

JOURNAL PAPERS

 

Prediction of blast induced ground vibrations in Karoun III power plant and dam: A neural network

 

 

M. Kamali; M. Ataei

Faculty of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Iran

 

 


SYNOPSIS

In this research, in order to predict the peak particle velocity (PPV) (as vibration indicator) caused by blasting projects in the excavations of the Karoun III power plant and dam, three techniques including statistical, empirical, and neural network were used and their results were interpreted and compared. First, multivariate regression analysis (MVRA) was used as statistical approach. Next, PPV was predicted using some widely used empirical models. Lastly, an artificial neural network was used. In the ANN model, maximum charge per delay, total charge per round, distance from blast site, direction of firing, blasthole length, number of blastholes, total delay in milliseconds, number of delay intervals, and average specific charge were taken into consideration as input parameters and consequently the PPV as output parameter. The results of the techniques were interpreted from two points of view. Firstly, the correlation between the observed data and predicted ones, secondly the total error between observed data and predicted ones. The MVRA had a satisfactory correlation but its error of estimation was comparatively very high. The empirical model had reliable correlation and a small error of estimation; in total the results of empirical method were more reliable than those of MVRA. Generally, the ANN approach showed very high correlation and a very small error. The results of this research indicated that the ANN model is the best predicting model for PPV in comparison with other approaches.

Keywords: Neural network, blasting, peak particle velocity, ground vibration, vibrations monitoring and excavation


 

 

“Full text available only in PDF format”

 

 

References

1. BASU, D. and SEN, M. Blast induced ground vibration norms-A critical review, National Seminar on Policies, Statutes & Legislation in Mines, 2005.         [ Links ]

2. INDIAN STANDARD INSTITUTE. Criteria for safety and design of structures subjected to underground blast, ISI Bull 1973, IS-6922.         [ Links ]

3. New BM. Ground vibration caused by civil engineering works. Transport and Road Research Laboratory, Research Report 53, 1986, p. 19        [ Links ]

4. CMRI .Vibration standards, Central Mining Research Institute, Dhanbad; 1993.         [ Links ]

5. KAHRIMAN, A. Analysis of Ground Vibrations Caused by Bench Blasting at Can Open-pit Lignite Mine in Turkey. Environmental Geology, vol. 41, 2001. pp. 653-661.         [ Links ]

6. CRANDELL, F.J. Ground vibration due to blasting and its effect upon structures. Journal of Boston society of civil engineers, 1949.         [ Links ]

7. DUVALL, W.I. AND PETKOF, B. Spherical propagation of explosion generated strain pulses in rock. USBM Report of Investigation 5483, 1959, p. 21.         [ Links ]

8. LANGEFORS, U. and KIHLSTROM, B. The modern technique of rock blasting. New York, Wiley, 1963.         [ Links ]

9. DAVIES, B., FARMER, I.W., and ATTEWELL, P.B. Ground vibrations from shallow sub-surface blasts. The Engineer, London 1964, pp. 553-9.         [ Links ]

10. AMBRASEYS, N.R. and HENDRON, A.J. Dynamic behaviour of rock masses: rock mechanics in engineering practices. London: Wiley, 1968.         [ Links ]

11. BUREAU OF INDIAN STANDARD. Criteria for safety and design of structures subjected to underground blast. ISI Bull IS-6922, 1973.         [ Links ]

12. GHOSH, A. and DAEMEN, J.K. A simple new blast vibration predictor. Proceedings of the 24th US symposium on rock mechanics, College Station, Texas, 1983. pp. 151-161.         [ Links ]

13. PAL ROY, P. Putting ground vibration predictors into practice. Colliery Guardian, vol. 241, 1993. pp. 63-7.         [ Links ]

14. DUVALL, W.I. and FOGELSON, D.E. Review of criteria for estimating damage to residences from blasting vibration. US Bureau of Mines R.I. 5968, 1962.         [ Links ]

15. LUNDBORG, N. Keeping the lid on flyrock in open-pit blasting. E/MJ, May, 1957.         [ Links ]

16. SMITH, P.D. and HETHERINGTON, J.G. Blast and ballistic loading of stracture. Elsevier Science. Butterworth-Heinemann Ltd., Oxford, 1994.         [ Links ]

17. HOLMBERG, R. and PERSSON, P.A. Design of tunnel perimeter blasthole patterns to prevent rock damage. Proc. IMM Tunnelling '79 Conference, London, 1979, pp. 280-283.         [ Links ]

18. GUPTA, R.N., PAL ROY., P., and SINGH, B. Prediction peak particle velocity and air pressure generated by buried explosion. Int. J. of mining and geological engineering. 1988.         [ Links ]

19. MOHAMED, M.T. Artificial neural network for prediction and control of blasting vibrations in Assiut (Egypt) limestone quarry. International Journal of Rock Mechanics & Mining Sciences, vol. 46, 2009. pp. 426-431.         [ Links ]

20. JIMENO, L.C., JIMENO, L.E., and CARCEDO, A.J.F. Drilling and Blasting of Rocks, A.A. Balkema, Rotterdam, 1995, pp. 333-365.         [ Links ]

21. SINGH, T.N. and SINGH, V. An intelligent approach to prediction and control ground vibration in mines. Geotechnical and Geological Engineering, vol. 23, 2005 pp. 249-262. doi 10.1007/s10706-004-7068-x.         [ Links ]

22. DYSART, P.S. and PULLI, J.J. Regional seismic event classification at the noress array: seismological measurements and the use of trained neural networks. Bull Seismol Soc Am, vol. 80, 1990. pp. 1910-33.         [ Links ]

23. YANG, Y. and ZHANG, Q. Analysis for the results of point load testing with artificial neural network. Proceedings of computing methods and advances in geomechanics, IACMAG, China, 1997, pp. 607-12.         [ Links ]

24. CAI, J.G. and ZHAO, J. Use of neural networks in rock tunneling. Proceedings of computing methods and advances in geomechanics, IACMAG, China, 1997, pp. 613-618.         [ Links ]

25. RUDAJEV, V. and CIZ, R. Estimation of mining tremor occurrence by using neural networks. Pure Appl Geophys, vol. 154, 1999. pp. 57-72.         [ Links ]

26. FINNIE, G.J. Using neural networks to discriminate between genuine and spurious seismic events in mines. Pure Appl Geophys, vol. 154, 1999. pp. 41-56.         [ Links ]

27. MUSIL, M. and PLESINGER, A. Discrimination between local micro earthquakes and quarry blasts by multi-layer perceptrons and Kohonen maps. Bull Seismol Soc Am, vol. 86, 1996. pp. 1077-90.         [ Links ]

28. MAULENKAMP, F. and GRIMA, M.A. Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip Hardness. Int J Rock Mech Min Sci, vol. 36, 1999. pp. 29-39.         [ Links ]

29. SINGH, V.K., SINGH, D., and SINGH, T.N. Prediction of strength properties of some schistose rock. Int J Rock Mech Min Sci, vol. 38, 2001. pp. 269-84.         [ Links ]

30. KHANDELWAL, M. and SINGH, T.N. Prediction of waste dump stability by an intelligent approach. Proceedings of the national symposium on new equipment-new technology, management and safety, ENTMS, Bhubaneshwar, 2002, pp. 38-45.         [ Links ]

31. AMBORZIC, T. and TURK, G. Prediction of subsidence due to underground mining by artificial neural networks. Computers & Geosciences, vol. 29, 2003. pp. 627-637.         [ Links ]

32. DENG, J., YUE, Z.Q., THAM, L.G., and ZHU, H.H. Pillar design by combining finite element methods, neural networks and reliability: a case study of the Feng Huangshan copper mine, China. International Journal of Rock Mechanics & Mining Sciences, vol. 40, 2003. pp. 585-599.         [ Links ]

33. MAITY, D. and SAHA, A. Damage assessment in structure from changes in static parameters using neural networks. Sadhana, vol. 29, 2004. pp. 315-27.         [ Links ]

34. SINGH, T.N., KANCHAN, R., SAIGAL, K., and VERMA, A.K. Prediction of P-wave velocity and anisotropic properties of rock using Artificial Neural Networks technique. J Sci Ind Res, vol. 63, 2004. pp. 32-8.         [ Links ]

35. MONJEZI, M., SINGH, T.N., KHANDELWAL, M., SINHA,S., SINGH, V., and Hosseini, I. Prediction and analysis of blast parameters using artificial neural network. Noise Vib Worldwide, vol. 37, 2006. pp. 8-16.         [ Links ]

36. MONJEZI, M. and DEHGHANI, H. Evaluation of effect of blasting pattern parameters on back break using neural networks. International Journal of Rock Mechanics & Mining Sciences, vol. 45, 2008. pp. 1446-1453.         [ Links ]

37. QIANG, W., SIYUAN, Y., and JIA, Y. The prediction of size-limited structures in a coal mine using Artificial Neural Networks. International Journal of Rock Mechanics & Mining Sciences vol. 45, 2008. pp. 999-1006.         [ Links ]

38. CHAKRABORTY, A.K., GUHA, P., CHATTOPADHYAY, B., PAL, S., and DAS, J. A Fusion Neural Network for Estimation of Blasting Vibration. N.R. Pal et al. (eds.): ICONIP 2004, LNCS 3316, pp. 008.1013, 2004. Springer-Verlag Berlin Heidelberg 2004.         [ Links ]

39. SINGH, T.N. Artificial neural network approach for prediction and control of ground vibrations in mines. Mining Technology (Trans. Inst. Min. Metall. A) September 2004, vol. 113. pp. 251-255. DOI 10.1179/037178404225006137.         [ Links ]

40. KHANDELWAL, M. and SINGH, T.N. Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach. Journal of Sound and Vibration, vol. 289, 2006. pp. 711-725.         [ Links ]

41. KHANDELWAL, M. AND SINGH, T.N. Evaluation of blast-induced ground vibration predictors. Soil Dynamics and Earthquake Engineering, vol. 27, 2007. pp. 116-125. doi:10.1016/j.soildyn.2006.06.004        [ Links ]

42. KHANDELWAL, M. and SINGH, T.N. Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Mining Sci, 2009. doi:10.1016/j.ijrmms.2009.03.004.         [ Links ]

43. LAWRENCE, S., GILES, C.L., and TSOI, A.C. Lessons in Neural Network Training: Training May be harder than Expected. Proceedings of the Fourteenth National Conference on Artificial Intelligence, AAAI-97, Menlo Park, California: AAAI Press, 1997, pp. 540-545.         [ Links ]

44. SARLE, W. Stopped Training and Other Remedies for Overfitting. Proceedings of the 27th Symposium on the Interface of Computing Science and Statistics, 1995, pp. 352-360.         [ Links ]

45. PRECHELT, L. Early Stopping-but when? Neural Networks: Tricks of the trade, Springer Berlin/Heidelberg, vol. 1524, 1998. pp. 55-69.         [ Links ]

 

 

Paper received Aug. 2009
Revised paper received Dec. 2010

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License