Skip to main content
SpringerLink
Log in

Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Blasting is the process of use of explosives to excavate or remove the rock mass. The main objective of blasting operation is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as ground vibration, flyrock and back-break. Therefore, proper predicting and subsequently optimizing these impacts may reduce damage on facilities and equipment. In this study, an artificial neural network (ANN) was developed to predict flyrock and back-break resulting from blasting. To do this, 97 blasting works in Delkan iron mine, Iran, were investigated and required blasting parameters were collected. The most influential parameters on flyrock and back-break, i.e. burden, spacing, hole length, stemming, and powder factor were considered as model inputs. Results of absolute error (Ea) and root mean square error (RMSE) (0.0137 and 0.063 for Ea and RMSE, respectively) reveal that ANN as a powerful tool can predict flyrock and back-break with high degree of accuracy. In addition, this paper presents a new metaheuristic approximation approach based on the ant colony optimization (ACO) for solving the problem of flyrock and back-break in Delkan iron mine. Considering changeable parameters of the ACO algorithm, blasting pattern parameters were optimized to minimize results of flyrock and back-break. Eventually, implementing ACO algorithm, reductions of 61 and 58 % were observed in flyrock and back-break results, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Bhandari S (1997) Engineering rock blasting operations. Taylor & Francis, Boca Raton

    Google Scholar 

  2. Raina AK, Murthy VMSR, Soni AK (2014) Flyrock in bench blasting: a comprehensive review. Bull Eng Geol Environ. doi:10.1007/s10064-014-0588-6

    Google Scholar 

  3. Raina AK, Haldar A, Chakraborty AK, Choudhury PB, Ramulu M, Bandyopadhyay C (2004) Human response to blast-induced vibration and air-overpressure: an Indian scenario. Bull Eng Geol Environ 63(3):209–214

    Article  Google Scholar 

  4. Marto A, Hajihassani M, Jahed Armaghani D, Tonnizam Mohamad E, Makhtar AM (2014) A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network. Sci World J, vol 2014 (Article ID 643715)

  5. Hajihassani M, Jahed Armaghani D, Monjezi M, Mohamad ET, Marto A (2015) Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environ Earth Sci. doi:10.1007/s12665-015-4274-1

    Google Scholar 

  6. Jahed Armaghani D, Tonnizam Mohamad E, Hajihassani M,Alavi Nezhad Khalil Abad SV, Marto A, Moghaddam MR(2015) Evaluation and prediction of flyrock resulting from blastingoperations using empirical and computational methods. Eng Comput. doi:10.1007/s00366-015-0402-5

  7. Institute of Makers of Explosives (IME) (1997) Glossary of commercial explosive industry terms. safety Publication, vol 12. Institute of Makers of Explosives, Washington DC, p 16

  8. Rustan A (1998) Rock blasting terms and symbols. A.A Balkema, Rotterda

  9. Bajpayee TS, Rehak TR, Mowrey GL, Ingram DK (2000) A summary of fatal accidents due to flyrock and lack of blast area security in surface mining, 1989–1999. In: Proceedings of the 27th annual conference on explosives and blasting technique, vol I. International Society of Explosives Engineers, Cleveland

  10. Fletcher LR, D’Andrea DV (1986) Control of flyrock in blastin. In: Proceedings of the 12th annual conference on explosives and blasting technique. International Society of Explosives Engineers, Cleveland, pp 167–177

  11. Rehak TR, Bajpayee TS, Mowrey GL, Ingram DK (2001) Flyrock issues in blasting. In: Proceedings of the 27th annual conference on explosives and blasting technique, vol I. International Society of Explosives Engineers, Cleveland, pp 165–175

  12. Shea CW, Clark D (1998) Avoiding tragedy: lessons to be learned from a flyrock fatality. Coal Age 103(2):51–54

    Google Scholar 

  13. Siskind DE, Kopp JW (1995) Blasting accidents in mines a 16 year summary. In: Proceedings of the 21st annual conference on explosives and blasting technique. International Society of Explosives Engineers, Cleveland, pp 224–239

  14. Massey JB, Siu KL (2003) Investigation of flyrock incident at Clearwater Bay Road on 6 June. Civ Eng Dept, Govt Hong Kong Special Admin Region, Hong Kong, p 49

  15. Gustafsson R (1973) Swedish blasting technique and mining SPI. Gothenburg, Sweden

    Google Scholar 

  16. Monjezi M, Amini khoshalan H, Yazdian Varjani A (2011) Optimization of open pit blast parameters using genetic algorithm. Int J Rock Mech Min Sci 48:864–869

  17. Rezaei M, Monjezi M, Yazdian Varjani A (2011) Development of a fuzzy model to predict flyrock in surface mining. Saf Sci 49:298–305

    Article  Google Scholar 

  18. Konya CJ (2003) Rock Blasting and Overbreak Control, 2nd edn. US Department of Transportation, Federal Highway Administration, Washington, DC

    Google Scholar 

  19. Gates W, Ortiz LT, Florez RM (2005) Analysis of rockfall and blasting backbreak problems. In: Proceedings of the 40th US symposium on rock mechanics. American Rock Mechanics Association, Alexandria, pp 671–80

  20. Monjezi M, Dehghani H (2008) Evaluation of effect of blasting pattern parameters on flyrock using neural networks. Int J Rock Mech Min Sci 45:1446–1453

    Article  Google Scholar 

  21. Esmaeili M, Osanloo M, Rashidinejad F, Aghajani Bazzazi A, Taji M (2012) Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Eng Comput. doi:10.1007/s00366-012-0298-2

  22. Lundborg N (1974) The hazards of fly rock in rock blasting. In: Report DS1974, Swedish Detonic Res Found (SveDeFo), Stockholm, p 12

  23. Roth JA (1979) A model for the determination of flyrock range as a function of shot condition. In: US Bureau of Mines Contract J0387242, Management Science Associates, p 61

  24. Hustrulid WA (1999) Blasting principles for open pit mining. In: General design concepts, vol 1. AA Balkema, Rotterda

  25. Momeni E, Nazir R, Jahed Armaghani D, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131

    Article  Google Scholar 

  26. Tonnizam Mohamad E, Jahed Armaghani D, Hajihassani M, Faizi K, Marto A (2013) A simulation approach to predict blasting-induced flyrock and size of thrown rocks. Electron J Geotech Eng 18:365–374

    Google Scholar 

  27. Jahed Armaghani D, Tonnizam Mohamad E, Momeni E, Narayanasamy MS, Mohd Amin MF (2014) An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite. Bull Eng Geol Environ. doi:10.1007/s10064-014-0687-4

    Google Scholar 

  28. Ghoraba S, Monjezi M, Talebi N, Moghadam MR, 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. J Zhejiang Univ Sci A. doi:10.1631/jzus.A1400252

  29. Garret JH (1994) Where and why artificial neural networks are applicable in civil engineering. J Comput Civil Eng. 8:129–130

    Article  Google Scholar 

  30. Grima MA, Babuška R (1999) Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int J Rock Mech Min Sci 36(3):339–349

    Article  Google Scholar 

  31. Tonnizam Mohamad E, Jahed Armaghani D, Momeni E, Alavi Nezhad Khalil Abad SV (2014) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ. doi:10.1007/s10064-014-0638-0

  32. Alvarez Grima M, Bruines PA, Verhoef PNW (2000) Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunnel undergr sp technol 15(3):259–269

    Article  Google Scholar 

  33. Ocak I, Seker SE (2013) Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes. Environ Earth Sci 70(3):1263–1276

    Article  Google Scholar 

  34. Gordan B, Jahed Armaghani D, Hajihassani M, Monjezi M (2015) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput. doi:10.1007/s00366-015-0400-7

    Google Scholar 

  35. 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

    Article  Google Scholar 

  36. Hajihassani M, Jahed Armaghani D, Sohaei H, Tonnizam Mohamad E, Marto A (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67

    Article  Google Scholar 

  37. Monjezi M, Bahrami A, Varjani AY (2010) Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int J Rock Mech Min Sci 47(3):476–480

    Article  Google Scholar 

  38. Monjezi M, Khoshalan HA, Varjani AY (2012) Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arab J Geosci 5(3):441–448

    Article  Google Scholar 

  39. Monjezi M, Bahrami A, Yazdian Varjani A, Sayadi AR (2011) Prediction and controlling of flyrock in blasting operation using artificial neural network. Arab J Geosci 4:421–425

    Article  Google Scholar 

  40. Mohammadnejad M, Gholami R, Sereshki F, Jamshidi A (2013) A new methodology to predict backbreak in blasting operation. Int J Rock Mech Min Sci 60:75–81

    Google Scholar 

  41. Sayadi A, Monjezi M, Talebi N, Khandelwal M (2013) A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak. J Rock Mech Geotech Eng 5(4):318–324

    Article  Google Scholar 

  42. Minjezi M, Ahmadi Z, Yazdian Varjani A, Khandelwal M (2013) Backbreak prediction in the Chadormalu iron mine using artificial neural network. Neural Comput Appl 23:1101–1107

    Article  Google Scholar 

  43. Ebrahimi E, Monjezi M, Khalesi MR, Jahed Armaghani D (2015) Prediction and optimization of backbreak and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ. doi:10.1007/s10064-015-0720-2

    Google Scholar 

  44. Shirani Faradonbeh R, Monjezi M, Jahed Armaghani D (2015) Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation. Eng Comput. doi:10.1007/s00366-015-0404-3

    Google Scholar 

  45. Amini H, Gholami R, Monjezi M, Torabi SR, Zadhesh J (2012) Evaluation of flyrock phenomenon due to blasting operation by support vector machine. Neural Comput Appl 21(8):2077–2085

    Article  Google Scholar 

  46. Khandelwal M, Monjezi M (2013) Prediction of backbreak in openpitblasting operations using the machine learning method. RockMech Rock Eng 46(2):389–396

    Article  Google Scholar 

  47. Jahed Armaghani D, Hajihassani M, Mohamad ET, Marto A, Noorani SA (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7(12):5383–5396

    Article  Google Scholar 

  48. Trivedi R, Singh TN, Raina A (2014) Prediction of blast-induced flyrock in Indian limestone mines using neural networks. J Rock MechGeotech Eng 6(5):447–454

    Article  Google Scholar 

  49. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26(1):29–41

    Article  Google Scholar 

  50. Dorigo M, Gambardella LM (1997) Ant colonies for the travelling salesman problem. BioSystems 43(2):73–81

    Article  Google Scholar 

  51. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evolut Comput 1(1):53–66

    Article  Google Scholar 

  52. Bullnheimer B, Hartl RF, Strauss C (1999) Applying the ant system to the vehicle routing problem. In: Meta-heuristics. Springer, US, pp 285–296

  53. Mazzeo S, Loiseau I (2004) An ant colony algorithm for the capacitated vehicle routing. Electron Notes Discrete Math 18:181–186

    Article  MathSciNet  MATH  Google Scholar 

  54. Zhang J, Hu X, Tan X, Zhong JH, Huang Q (2006) Implementation of an ant colony optimization technique for job shop scheduling problem. Trans Inst Measur Control 28(1):93–108

    Article  Google Scholar 

  55. Dréo J, Siarry P (2004) Continuous interacting ant colony algorithm based on dense heterarchy. Future Gener Comput Syst 20(5):841–856

    Article  Google Scholar 

  56. Kong M, Tian P (2006) Application of ACO in continuous domain. In: Advances in natural computation. Springer, Berlin, pp 126–135

  57. Shishvan MS, Sattarvand J (2015) Long term production planning of open pit mines by ant colony optimization. Eur J Oper Res 240(3):825–836

    Article  Google Scholar 

  58. Zhu J, Xiao-ping M (2009) Safety evaluation of human accidents in coal mine based on ant colony optimization and SVM. Proced Earth Planet Sci 1(1):1418–1424

    Article  Google Scholar 

  59. Gao W (2015) Forecasting of rockbursts in deep underground engineering based on abstraction ant colony clustering algorithm. Nat Hazards 76(3):1625–1649

    Article  Google Scholar 

  60. McCulloch Warren S, Pitts Walter (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  MathSciNet  MATH  Google Scholar 

  61. Haykin S (1999) Neural networks, 2nd edn. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  62. Engelbrecht AP (2007) Computational intelligence: an introduction. Wiley, New York

  63. Momeni E, Jahed Armaghani D, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63

    Article  Google Scholar 

  64. Jahed Armaghani D, Hajihassani M, Monjezi M, Mohamad ET, Marto A, Moghaddam MR (2015) Application of two intelligent systems in predicting environmental impacts of quarry blasting. Arab J Geosci. doi:10.1007/s12517-015-1908-2

    Google Scholar 

  65. Poulton MM (2002) Neural networks as an intelligence amplification tool: a review of applications. J Geophys 67(3):979–993

    Article  Google Scholar 

  66. 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

    Article  Google Scholar 

  67. Demuth H, Beale M (2002) Neural network toolbox user’s guide version 4. The Math Works, USA

  68. Negnevitsky M (2002) Artificial intelligence: a guide to intelligent systems. Addison-Wesley, England

    Google Scholar 

  69. Simpson PK (1990) Artificial neural system: foundation, paradigms, applications and implementations. Pergamon, New York

    Google Scholar 

  70. Dorigo M (1992) Learning and natural algorithms, in electrical engineering. Politecnico di Milano, Italy

  71. Hajizadeh Y, Christie M, Demyanov V (2011) Ant colony optimization for history matching and uncertainty quantification of reservoir models. J Pet Sci Eng 77(1):78–92

    Article  Google Scholar 

  72. Socha K, Dorigo M (2008) Ant colony optimization for continuous domains. Eur J Oper Res 185(3):1155–1173

    Article  MathSciNet  MATH  Google Scholar 

  73. Dorigo M, Gambardella LM (2006) Ant colony optimization and swarm intelligence. Springer, Berlin

    Book  Google Scholar 

  74. Rafig MY, Bugmann G, Easterbrook DJ (2001) Neural network design for engineering applications. Comput Struct 79:541–1552

    Google Scholar 

  75. Nelson M, Illingworth WT (1990) A practical guide to neural nets. Addison-Wesley, Reading

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Tarbiat Modares University, Tehran, 14115-143, Iran

    Amir Saghatforoush, Masoud Monjezi & Roohollah Shirani Faradonbeh

  2. Young Researchers and Elite Club, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran

    Danial Jahed Armaghani

Authors
  1. Amir Saghatforoush
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Masoud Monjezi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roohollah Shirani Faradonbeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Danial Jahed Armaghani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masoud Monjezi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saghatforoush, A., Monjezi, M., Shirani Faradonbeh, R. et al. Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting. Engineering with Computers 32, 255–266 (2016). https://doi.org/10.1007/s00366-015-0415-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-015-0415-0

Keywords

Search

Navigation