Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation

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

Abstract

In addition to all benefits of blasting in mining and civil engineering applications, it has some undesirable environmental impacts. Backbreak is an unwanted phenomenon of blasting which can cause instability of mine walls, decreasing efficiency of drilling, falling down of machinery, etc. Recently, the use of new approaches such as artificial intelligence (AI) is greatly recommended by many researchers. In this paper, a new AI technique namely genetic programing (GP) was developed to predict BB. To prepare a sufficient database, 175 blasting works were investigated in Sungun copper mine, Iran. In these operations, the most influential parameters on BB including burden, spacing, stemming length, powder factor and stiffness ratio were measured and used to develop BB predictive models. To demonstrate capability of GP technique, a non-linear multiple regression (NLMR) model was also employed for prediction of BB. Value account for (VAF), root mean square error (RMSE) and coefficient of determination (R 2) were used to control the capacity performance of the predictive models. The performance indices obtained by GP approach indicate the higher reliability of GP compared to NLMR model. RMSE and VAF values of 0.327 and 97.655, respectively, for testing datasets of GP approach reveal the superiority of this model in predicting BB, while these values were obtained as 0.865 and 81.816, respectively, for NLMR model.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

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

    Google Scholar 

  2. Hajihassani M, Jahed Armaghani D, Marto A, Tonnizam Mohamad E (2014) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ. doi:10.1007/s10064-014-0657-x

    Google Scholar 

  3. 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 blasting operations using empirical and computational methods. Eng Comput. doi:10.1007/s00366-015-0402-5

    Google Scholar 

  4. Khandelwal M, Monjezi M (2013) Prediction of backbreak in open-pit blasting operations using the machine learning method. Rock Mech Rock Eng 46(2):389–396

    Article  Google Scholar 

  5. 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. doi:10.1007/s12517-013-1174-0

    Google Scholar 

  6. 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 

  7. 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 

  8. 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 

  9. 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 

  10. Jimeno CL, Jimeno EL, Carcedo FJA (1995) Drilling and blasting of rocks. Balkema, Rotterdam

    Google Scholar 

  11. Firozadj A, Ebrahimi MA, mansouri H (2006) Application of Controlled Blasting (Pre Splitting) in Sarcheshmeh copper mine. In: 8th international symposia on Rock Fragmentation by Blasting, Chile

  12. Konya CJ, Walter EJ (1991) Rock blasting and overbreak control. United States Department of Transportation, McClean

    Google Scholar 

  13. Gate WCB, Ortiz LT, Florez RM (2005) Analysis of rockfall and blasting backbreak problems, US 550, Molas Pass, CO. In: Proceedings of the 40th US symposium on rock mechanics, ARMA/USRMS, 2005, Anchorage, Alaska, pp 671–680

  14. Monjezi M, Rezaei M, Yazdian A (2010) Prediction of backbreak in open-pit blasting using fuzzy set theory. Expert Syst Appl 37:2637–2643

    Article  Google Scholar 

  15. Monjezi 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 

  16. 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

    Google Scholar 

  17. Faramarzi F, Ebrahimi Farsangi MA, Mansouri H (2013) An RES-based model for risk assessment and prediction of backbreak in bench blasting. Rock Mech Rock Eng 46:877–887

    Article  Google Scholar 

  18. Sari M, Ghasemi E, Ataei M (2014) Stochastic modeling approach for the evaluation of backbreak due to blasting operations in open pit mines. Rock Mech Rock Eng 47:771–783

    Article  Google Scholar 

  19. Beiki M, Bashari A, Majdi A (2010) Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network. Int J Rock Mech Min Sci 47:1091–1103

    Article  Google Scholar 

  20. Baykasoglu A, Gullu H, Canakci H, Ozbakır L (2008) Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 35:111–123

    Article  Google Scholar 

  21. Asadi M, Eftekhari M, Bagheripour MH (2011) Evaluating the strength of intact rocks through genetic programming. Appl Soft Comput 11:1932–1937

    Article  Google Scholar 

  22. 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 

  23. 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 5:318–324

    Article  Google Scholar 

  24. Monjezi M, Rizi SH, Majd VJ, Khandelwal M (2014) Artificial neural network as a tool for backbreak prediction. Geotech Geol Eng 32(1):21–30

    Article  Google Scholar 

  25. Koza JR (2008) www.genetic-programming.com, The home page of John R. Koza at Genetic Programming Inc

  26. Aytek A, Kisi O (2008) A genetic programming approach to suspended sediment modeling. J Hydrol 351:288–298

    Article  Google Scholar 

  27. Sette S, Boullart L (2001) Genetic programming: principles and applications. Eng Appl Artif Intell 14:727–736

    Article  Google Scholar 

  28. Liong SY, Gautam TR, Khu ST, Babovic V, Keijzer M, Muttil N (2002) Genetic programming, A new paradigm in rainfall runoff modeling. J Am Water Res Assoc 38(3):705–718

    Article  Google Scholar 

  29. Ghasemi E, Sari M, Ataei M (2012) Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines. Int J Rock Mech Min Sci 52:163–170

    Article  Google Scholar 

  30. Wilson JM, Moxon NT (1988) The development of low energy ammonium nitrate based explosives. In: Proceedings of the Australasian Institute of Mining and Metallurgy, Melbourne, Australia, pp 27–32

  31. Jenkins SS (1981) Adjusting blast design for best results. Pit and Quarry, Rotterdam

    Google Scholar 

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

    Article  Google Scholar 

  33. Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York

    Google Scholar 

  34. Looney CG (1996) Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8(2):211–226

    Article  Google Scholar 

  35. Nelson M, Illingworth WT (1990) A Practical Guide to Neural Nets. Addison- Wesley, Reading

    Google Scholar 

  36. Ceryan N, Okkan U, Kesimal A (2012) Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks. Environ Earth Sci 68:807–819

    Article  Google Scholar 

  37. Yagiz S, Gokceoglu C, Sezer E, Iplikci S (2009) Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng Appl Artif Intel 22(4):808–814

    Article  Google Scholar 

  38. 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 

  39. Yagiz S, Gokceoglu C (2010) Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness. Expert Sys Appl 37(3):2265–2272

    Article  Google Scholar 

  40. SPSS Inc. (2007). SPSS for Windows (Version 16.0). Chicago: SPSS Inc

  41. Yang Y, Zang O (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Eng 30:207–222

    Article  Google Scholar 

  42. Sarma KS (1994) Models for assessing the blasting performance of explosives. Ph.D. thesis. University of Queensland, Brisbane

Download references

Author information

Authors and Affiliations

  1. Department of Mining, Tarbiat Modares University, 14115-143, Tehran, Iran

    Roohollah Shirani Faradonbeh & Masoud Monjezi

  2. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, UTM Skudai, 81310, Johor Bahru, Johor, Malaysia

    Danial Jahed Armaghani

Authors
  1. Roohollah Shirani Faradonbeh
    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. 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

Shirani Faradonbeh, R., Monjezi, M. & Jahed Armaghani, D. Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation. Engineering with Computers 32, 123–133 (2016). https://doi.org/10.1007/s00366-015-0404-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-015-0404-3

Keywords

Search

Navigation