Skip to main content
SpringerLink
Log in

Development of overbreak prediction models in drill and blast tunneling using soft computing methods

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

Abstract

According to the human requirement, such as mineral extraction, transportation, etc., tunnel construction is an evident need. Mining and tunneling activities are full of problems with uncertainty, which make these problems complex and difficult. Overbreak is one of these problems that we are encountering in the tunneling process, particularly in the drill and blast method. Overbreak causes to decreasing the safety and increasing the operational costs. Therefore, it must be minimized. Overbreak prediction is the first step to decreasing the damaging effects of this phenomenon. Overbreak influencing factors are classified into two groups of uncontrollable factors and controllable factors. In this study, 267 sets of causing factors and overbreak data were used to overbreak prediction model’s development using the multiple linear and nonlinear regression analysis, artificial neural network, fuzzy logic, adaptive neuro-fuzzy inference system, and support vector machine. The determination coefficient values of these models have been obtained as 0.84, 0.87, 0.93, 96, 0.97, and 0.87, respectively. The results illustrated that the fuzzy and adaptive neuro-fuzzy inference system models have done more appropriate prediction than other prediction models.

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
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Murthy VMSR, Dey K, Raitani R (2003) Prediction of over break in underground tunnel blasting a case study. J Can Tunn 109–115

  2. Jang H, Topal E (2013) Optimizing over break prediction based on geological parameters comparing multiple regression analysis and artificial neural network. Tunn Undergr Space Technol 38:161–169

    Article  Google Scholar 

  3. Mohammadi M, Farouq MH, Mirzapour B, Hajiantilaki N (2015) Use of fuzzy logic for minimizing overbreak in underground blasting operations—a case study of Alborz Tunnel, Iran. Int J Min Sci Technol 25(3):439–445

    Article  Google Scholar 

  4. Jang H, Topal E (2014) A review of soft computing technology applications in several mining problems. Appl Soft Comput 22:638–651

    Article  Google Scholar 

  5. Rustan AP (1998) Micro-sequential contour blasting-how does it influence the surrounding rock mass? Eng Geol 49:303–313

    Article  Google Scholar 

  6. Chakraborty AK, Jethwa JL, Palthankar AG (1994) Assessing the effects of joint orientation and rock mass quality on fragmentation and over break in tunnel blasting. Tunn Undergr Space Technol 9(4):471–482

    Article  Google Scholar 

  7. Ibarra JA, Maerz NH, Franklin JA (1996) Over break and under break in underground openings Part2: causes and implications. Geotech Geol Eng 14:325–340

    Article  Google Scholar 

  8. Kim Y, Moon H (2013) Application of the guideline for over break control in granitic rock mass in Korean tunnels. Tunn Undergr Space Technol 35:67–77

    Article  Google Scholar 

  9. Singh SP, Xavier P (2005) Causes, impact and control of over break in underground excavation. Tunn Undergr Space Technol 20:63–71

    Article  Google Scholar 

  10. Schmitz RM, Viroux S, Charlier R, Hick S (2006) The role of rock mechanics in analyzing over break: application to the soumagne tunnel. EURock 2006-Multiphysics coupling and long term behavior in rock mechanics. Taylor & Francis, London, pp 631–636. doi:10.1201/9781439833469.ch92

  11. Dey K, Murthy VMSR (2012) Prediction of blast induced over break from uncontrolled burn-cut blasting in tunnel driven through medium rock class. Tunn Undergr Space Technol 28:49–56

    Article  Google Scholar 

  12. Shiwei S, Lie N, Shulin D, Yan X (2013) Influence mechanism of lamella joints on tunnel blasting effect. Res J Appl Sci Eng Technol 5(20):4905–4908

    Google Scholar 

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

    Article  Google Scholar 

  14. Khandelwal M, Kankar PK, Harsha SP (2010) Evaluation and prediction of blast induced ground vibration using support vector machine. Int J Min Sci Technol 20:64–70

    Google Scholar 

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

    Article  Google Scholar 

  16. Monjezi M, Bahrami A, Varjani AY, Sayadi AK (2011) Prediction and controlling of fly rock in blasting operation using artificial neural network. Arab J Geosci 4:421–425

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Fişne A, Kuzu C, Hüdaverdi T (2011) Prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environ Monit Assess 174:461–470

    Article  Google Scholar 

  19. Rezaei M, Monjezi M, Yazdanian VA (2012) Development of fuzzy model to predict fly rock in surface mining. Saf Sci 49(298):305

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. 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 

  23. Sun S, Liu J, Wei J (2013) Prediction of overbreak blocks in tunnels based on the wavelet neural network method and the geological statistics theory. Math Probl Eng. doi:10.1155/2013/706491

  24. Hajihassani M, Armaghani DJ, Sohaei H, Tonnizam EM, 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 

  25. Esmaeili M, Osanloo M, Rashidinejad F, Aghajani AB, Taji M (2014) Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting. Eng Comput 30(4):549–558

    Article  Google Scholar 

  26. Armaghani DJ, Hasanipanah M, Mohammad ET (2016) A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput 32(1):155–171

    Article  Google Scholar 

  27. Ghasemi E, Kalhori H, Bagherpour R (2016) A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting. Eng Comput 32(4):607–614

    Article  Google Scholar 

  28. Ghasemi E, Amnieh HB, Bagherpour R (2016) Assessment of backbreak due to blasting operation in open pit mines: a case study. Environ Earth Sci 75:552–563

    Article  Google Scholar 

  29. Ebrahimi E, Monjezi M, Khalesi MR, Armaghani DJ (2016) Prediction and optimization of backbreak and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull Eng Geol Environ 75(1):27–36

    Article  Google Scholar 

  30. Dehghani H, Shafaghi M (2017) Prediction of blast-induced flyrock using differential evolution algorithm. Eng Comput 33(1):149–158

    Article  Google Scholar 

  31. Hasanipanah M, Naderi R, Kashir J, Noorani SA, Zeynali AAQ (2017) Prediction of blast-produced ground vibration using particle swarm optimization. Eng Comput. doi:10.1007/s00366-016-0462-1

    Google Scholar 

  32. Hasanipanah M, Shahnazari A, Amnieh HB, Armaghani DJ (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO-SVR model. Eng Comput. doi:10.1007/s00366-016-0453-2

    Google Scholar 

  33. Fouladgar N, Hasanipanah M, Amnieh HB (2017) Application of cuckoo search algorithm to estimate peak particle velocity in mine blasting. Eng Comput. doi:10.1007/s00366-016-0463-0

    Google Scholar 

  34. Hasanipanah M, Golzar SB, Larki IA, Maryaki MY, Ghahremanians T (2017) Estimation of blast-induced ground vibration through a soft computing framework. Eng Comput. doi:10.1007/s00366-017-0508-z

    Google Scholar 

  35. Faradonbeh RS, Monjezi M (2017) Prediction and minimization of blast-induced ground vibration using two robust meta-heuristic algorithm. Eng Comput. doi:10.1007/s00366-017-0501-6

    Google Scholar 

  36. Armaghani DJ, Mohamad ET, Narayanasamy MS, Narita N (2017) Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition. Tunn Undergr Space Technol 63:29–43

    Article  Google Scholar 

  37. Mahtab MA, Rossier K, Kalamaras GS, Grasso P (1997) Assessment of geological over break for tunnel design and contractual claims. Int J Rock Mech Min Sci 34(3–4):185-e1–185-e13

    Google Scholar 

  38. Singh B, Goel RK (2011) Engineering rock mass classification: tunneling, foundations, and landslides. Elsevier Inc, UK

    Google Scholar 

  39. Armaghani DJ, Amin MFM, Yagiz S, Shirani R, Abdullah RA (2014) Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. Int J Rock Mech Min Sci 85:174–186

    Google Scholar 

  40. Yurdakul M, Gopalakrishnan K, Akdas H (2014) Prediction of specific cutting energy in natural stone cutting processes using the neuro-fuzzy methodology. Int J Rock Mech Min Sci 67:127–135

    Google Scholar 

  41. Abdulshahed AM, Longstaff AP, Fletcher S (2015) The application of ANFIS prediction models for thermal error compensation on CNC machine tools. Appl Soft Comput 27:158–168

    Article  Google Scholar 

  42. Shevade SK, Keerthi SS, Bhattacharyya C, Murthy KRA (2000) Improvements to the SMO algorithm for SVM regression. IEEE Trans Neural Netw 11(5):1188–1193

    Article  MATH  Google Scholar 

  43. Zhao CY, Zhang HX, Zhabg XY, Liu MC, Hu ZD, Fan BT (2006) Application of support vector machine(SVM) for prediction toxic activity of different data sets. J Toxicol 217:105–119

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful to Mohammad Farouq Hosseini, Mohammad Mohammadi, Kaushik Dey, and V.M.S.R. Murthy for the use of their researches information in this paper. In addition, we are grateful to the chief executive of Eastern Alborz Coal Corporation due to preparing the convenience condition for data gathering.

Author information

Authors and Affiliations

  1. Department of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Daneshgah Blvd, Shahrood, 3619995161, Iran

    Adel Mottahedi, Farhang Sereshki & Mohammad Ataei

Authors
  1. Adel Mottahedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farhang Sereshki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Ataei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adel Mottahedi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mottahedi, A., Sereshki, F. & Ataei, M. Development of overbreak prediction models in drill and blast tunneling using soft computing methods. Engineering with Computers 34, 45–58 (2018). https://doi.org/10.1007/s00366-017-0520-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-017-0520-3

Keywords

Search

Navigation