Blasting Pattern Design for Decreasing the Ground Vibration Using Genetic Algorithm

Authors

1 Assistant Professor, Dept. of Mining Engineering, Hamedan University of Technology, Hamedan, Iran

2 M.Sc, Dept. of Mining Engineering, Hamedan University of Technology, Hamedan, Iran

Abstract

Ground vibration is one of the most unfavorable consequences of the blasting operation in open pit mines, which assign about 40 percent of explosive energy. Ground vibration may cause some unsuitable effects such as destroying the surface structures, damaging the free face and generate back breaks, generating the over-size boulders and imposing additional costs to the mine because of the secondary blasting. Optimum blasting pattern design can help to reduce the above mentioned problems. Due to multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriate for blasting pattern design. In this paper, using a combination of the Grey analysis and Genetic algorithm, addition to developing a new equation for estimating the ground vibration in Sarcheshmeh Copper Mine, blasting pattern is presented. The results show that with applying the proposed blasting pattern the average ground vibration will be decreased about 55 percent.

Keywords

Main Subjects

References

[1]     Jimeno, C. L., Jimeno, E. L., and Carcedo, F. J. A. (1995). “Drilling and blasting of rocks”. Rotterdam: Balkema, pp. 340.
[2]     Dehghani, H., and Ataee-pour, M. (2011). “Development of a model to predict peak particle velocity in a blasting operation”. International Journal of Rock Mechanics and Mining Science, 48: 51–58.
[3]     Hajihassani, M., Armaghani, D. J., Marto, A., and Mohamad, E. T. (2015). “Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm”. Bulletin of Engineering Geology and the Environment, 74(3): 873-886.
[4]     Monjezi, M., Ghafurikalajahi, M., and Bahrami, A. (2011). “Prediction of blast-induced ground vibration using artificial neural networks”. Tunnelling and Underground Space Technology, 26(1): 46-50.
[5]     Saadat, M., Khandelwal, M., and Monjezi, M. (2014). “An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran”. Journal of Rock Mechanics and Geotechnical Engineering, 6(1): 67–76.
[6]     Mohammad, M. T. (2009). “Artificial neural network for prediction and control of blasting vibration in Assiut (Egypt) limestone quarry”. International Journal of Rock Mechanics and Mining Sciences, 46(2): 426–431.
[7]     Khandelwal, M., and Singh, T. N. (2009). “Prediction of blast-induced ground vibration using artificial neural network”. International Journal of Rock Mechanics and Mining Sciences, 46: 1214–1222.
[8]     Singh, P. K., and Roy, M. P. (2010). “Damage to surface structures due to blast vibration”. International Journal of Rock Mechanics and Mining Sciences, 47(6): 949–961.
[9]     Mohan, V., and Dey, N. C. (2014). “Assessment of Ground Vibration and Plotting of Contour Map of Surface Mine Blasting”. Journal of the Institution of Engineers (India): Series D, 95(1): 1–6.
[10]  Armaghani, D. J., Hajihassani, M., Mohamad, E. T., Marto, A., and Noorani, S. A. (2014). “Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization”. Arabian Journal of Geosciences, 7(12): 5383-5396.
[11]  Ghoraba, S., Monjezi, M., Talebi, N., Moghadam, M. R., and Jahed Armaghani, D. (2015). “Prediction of ground vibration caused by blasting operations through a neural network approach: a case study of Gol-E-Gohar Iron Mine Iran”. Journal of Zhejiang University Science A, 10: 1631. DOI: 10.1631/jzus.A1400252.
[12]  Ataei, M., and Sereshki, F. (2017). “Improved prediction of blast-induced vibrations in limestone mines using Genetic Algorithm”. Journal of Mining and Environment, 8(2): 291-304.
[13]  Hasanupanah, M., Bakhshandeh Amnieh, H., Khamesi, H., Jahed Armaghani, D., Bagheri Golzar, S., and Shahnazar, A. (2018). “Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system”. International Journal of Environmental Science and Technology, 15(3): 551–560.
[14]  Ragam, P., and Nimaje, D. S. (2018). “Evaluation and prediction of blast-induced peak particle velocity using artificial neural network: A case study”. Noise and Vibration Worldwide, 43(3): 111-119.
[15]  Andrew, A. M. (2011). “Why the world is grey?”. Grey Systems: Theory and Application, 1(2): 112-116.
[16]  Deng, J. L. (1982). “Control problems of grey systems”. Systems & Control Letters, 1(5): 288-294.
[17]  Haupt, R. L., and Haupt, S. E. (2004). “Practical Genetic Algorithms”. 2nd ed. Hoboken, NJ: Wiley, pp. 272.
[18]  کیا، م.؛ 1391؛"الگوریتم‎های ژنتیک در متلب". انتشارات دانشگاهی کیان، 192 صفحه.

Files

History

  • Receive Date: 21 January 2019
  • Revise Date: 22 February 2019
  • Accept Date: 01 May 2019

Share

How to cite

Statistics