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Application of cuckoo search algorithm to estimate peak particle velocity in mine blasting

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Abstract

Ground vibration is one of the most undesirable effects of blasting operation in surface mines. Therefore, it seems that the prediction of ground vibrations with a high degree of accuracy is necessary to reduce environmental effects. This article proposes a novel swarm intelligence algorithm based on cuckoo search (NSICS) to create a precise equation for predicting the ground vibration produced by blasting operations in Miduk copper mine, Iran. To evaluate the proposed NSICS model, several empirical equations were also utilized. In this regard, 85 blasting events were considered, and the values of two effective parameters on the ground vibration, namely, maximum charge used per delay and distance between blast face and monitoring station, were measured. In addition, the values of peck particle velocity (PPV), as a vibration descriptor, were recorded in each blasting. Two performance indices, i.e., root mean square error and coefficient of multiple correlation (R 2), were used to determine the performance capacity of the proposed models. Comparing the values predicted by the models demonstrated that the proposed equation by NSICS is more reliable than empirical equations in predicting the PPV.

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References

  1. Hustrulid W (1999) Blasting principles for open pit mining: general design concepts, vol 1. Balkema, Rotterdam

  2. Singh TN, Dontha LK, Bhardwaj V (2008) Study into blast vibration and frequency using ANFIS and MVRA. Min Technol 117(3):116–121

    Article  Google Scholar 

  3. Khandelwal M (2011) Blast-induced ground vibration prediction using support vector machine. Eng Comput 27:193–200. doi:10.1007/s00366-010-0190-x

    Article  Google Scholar 

  4. Ghasemi E, Ataei M, Hashemolhosseini H (2012) Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. J Vib Control 19:755–770

    Article  Google Scholar 

  5. Monjezi M, Hasanipanah M, Khandelwal M (2013) Evaluation and prediction of blast-induced ground vibration at Shur river dam, Iran, by artificial neural network. Neural Comput Appl 22:1637–1643

    Article  Google Scholar 

  6. Trivedi R, Singh TN, Raina AK (2014) Prediction of blast-induced flyrock in Indian limestone mines using neural networks. J Rock Mech Geotech Eng 6:447–454

    Article  Google Scholar 

  7. Jahed Armaghani D, Hajihassani M, Sohaei H, Mohamad ET, Marto A, Motaghedi H, Moghaddam MR (2015) Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arab J Geosci. doi:10.1007/s12517-015-1984-3

    Google Scholar 

  8. Singh TN, Singh A, Singh CS (1994) Prediction of ground vibration induced by blasting. Indian Min Eng J 31–34:16

    Google Scholar 

  9. Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222

    Article  Google Scholar 

  10. Khandelwal M, Singh TN (2013) Application of an expert system to predict maximum explosive charge used per delay in surface mining. Rock Mech Rock Eng 46(6):1551–1558

    Article  Google Scholar 

  11. Hasanipanah M, Monjezi M, Shahnazar A, Jahed Armaghani D, Farazmand A (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289–297

    Article  Google Scholar 

  12. Khandelwal M, Singh TN (2007) Evaluation of blast-induced ground vibration predictors. Soil Dyn Earthq Eng 27:116–125

    Article  Google Scholar 

  13. Hajihassani M, Jahed Armaghani D, Monjezi M, Tonnizam Mohamad E, Marto A (2015) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci 74(4):2799–2817

    Article  Google Scholar 

  14. Khandelwal M, Saadat M (2015) A dimensional analysis approach to study blast-induced ground vibration. Rock Mech Rock Eng 48:727–735. doi:10.1007/s00603-014-0604-y

    Article  Google Scholar 

  15. Amiri M, Bakhshandeh Amnieh H, Hasanipanah M, Mohammad Khanli L (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng Comput. doi:10.1007/s00366-016-0442-5

    Google Scholar 

  16. Singh TN, Singh V (2005) An intelligent approach to prediction and control ground vibration in mines. Geotech Geol Eng 23:249–262

    Article  Google Scholar 

  17. Khandelwal M, Kumar DL, Yellishetty M (2011) Application of soft computing to predict blast-induced ground vibration. Eng Comput 27(2):117–125

    Article  Google Scholar 

  18. Konya CJ, Walter EJ (1990) Surface blast design. Prentice Hall, Englewood Cliffs

  19. ISRM (1992) Suggested method for blast vibration monitoring. Int J Rock Mech Min Geomech Abstr 29:145–156

    Article  Google Scholar 

  20. Duvall WI, Petkof B (1959) Spherical propagation of explosion generated strain pulses in rock. US Bureau of Mines Report of Investigation 5483

  21. Langefors U, Kihlstrom B (1963) The modern technique of rock blasting. Wiley, New York

    Google Scholar 

  22. Davies B, Farmer IW, Attewell PB (1964) Ground vibrations from shallow sub-surface blasts. Engineer 217:553–559

    Google Scholar 

  23. Ambraseys NR, Hendron AJ (1968) Dynamic behavior of rock masses: rock mechanics in engineering practices. Wiley, London

    Google Scholar 

  24. Ghosh A, Daemen JK (1983) A simple new blast vibration predictor. In: Proceedings of the 24th US Symposium on Rock Mechanics, Texas, pp 151–161

  25. Gupta RN, Roy P, Singh B (1987) On a blast induced blast vibration predictor for efficient blasting. In: Proceedings of the 22nd international conference on safety in Mines Research Institute, Beijing, pp 1015–1021

  26. Pal Roy P (1991) Vibration control in an opencast mine based on improved blast vibration predictors. Min Sci Technol 12(2):157–165

    Article  Google Scholar 

  27. Rai R, Singh TN (2004) A new predictor for ground vibration prediction and its comparison with other predictors. Indian J Eng Mater Sci 11(3):178–184

    Google Scholar 

  28. Jahed Armaghani D, Hajihassani M, Mohamad ET, Marto A, Noorani SA (2013) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci. doi:10.1007/s12517-013-1174-0

    Google Scholar 

  29. Saadat M, Khandelwal M, Monjezi M (2014) An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran. J Rock Mech Geotech Eng 6:67–76

    Article  Google Scholar 

  30. Mohamed MT (2011) Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations. Int J Rock Mech Min Sci 48:845–851

    Article  Google Scholar 

  31. Dehghani H, Ataee-pour M (2011) Development of a model to predict peak particle velocity in a blasting operation. Int J Rock Mech Min Sci 48:51–58

    Article  Google Scholar 

  32. Verma AK, Singh TN (2013) Comparative study of cognitive systems for ground vibration measurements. Neural Comput Appl 22(Suppl 1):S341–S350

    Article  Google Scholar 

  33. Armaghani Jahed, Momeni E, Abad SVANK, Khandelwal M (2015) Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environ Earth Sci. doi:10.1007/s12665-015-4305-y

    Google Scholar 

  34. Iphar M, Yavuz M, Ak H (2008) Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environ Geol 56(1):97–107

    Article  Google Scholar 

  35. Yang XS, Deb S (2009) Cuckoo search via lévy flights. World congress on nature and biologically inspired computing. In: IEEE, pp 210–214

  36. Yang XS, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Model Numer Optim 4:330–343

    MATH  Google Scholar 

  37. Gandomi AH, Talatahari S, Yang XS, Deb S (2012) Design optimization of truss structures using cuckoo search algorithm. Struct Des Tall Spec Build 22:1330–1349

    Article  Google Scholar 

  38. Yang XS, Deb S (2012) Cuckoo search for inverse problems and topology optimization. In: Proceedings of international conference on advances in computing. Advances in intelligent systems and computing, vol 174:291–295. doi:10.1007/978-81-322-0740-5_35

  39. Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29:17–35

    Article  Google Scholar 

  40. Fouladgar N, Lotfi Sh (2015) A novel approach for optimization in dynamic environments based on modified cuckoo search algorithm. Soft Comput. doi:10.1007/s00500-015-1951-7

    Google Scholar 

  41. Yang XS (2010) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, Frome

    Google Scholar 

  42. Fouladgar N, Lotfi Sh (2015) A novel swarm intelligence algorithm based on cuckoo search algorithm (NSICS). In: Intelligent computing theories and methodologies, Springer, New York, pp 587–596.

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Acknowledgments

The authors would like to extend their appreciation to manager, engineers, and personnel of Miduk copper mine as well as Mr. Alireza Farazmand for providing the needed information and facilities that made this research possible.

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Correspondence to Mahdi Hasanipanah.

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Fouladgar, N., Hasanipanah, M. & Bakhshandeh Amnieh, H. Application of cuckoo search algorithm to estimate peak particle velocity in mine blasting. Engineering with Computers 33, 181–189 (2017). https://doi.org/10.1007/s00366-016-0463-0

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  • DOI: https://doi.org/10.1007/s00366-016-0463-0

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