Advertisement

Engineering with Computers

, Volume 35, Issue 1, pp 35–45 | Cite as

Designing a fuzzy cognitive map to evaluate drilling and blasting problems of the tunneling projects in Iran

  • E. BakhtavarEmail author
  • Y. Shirvand
Original Article
  • 74 Downloads

Abstract

The study of drilling and blasting processes in excavation projects, especially in Iran, demonstrates various challenges and problems that eventually affect the technical and economic aspects of project performance. This paper introduces a methodology based on a computer-based fuzzy cognitive map approach to find and prioritize the problematic drilling and blasting factors in tunneling projects‏ in Iran. A particular cognitive map of the problem was designed by use of 34 problematic factors that selected by tunneling engineers in Iran. In the designed map, the weights of the problematic factors and their interactions were considered on the basis of the opinions of engineers. The designed map was finally solved by considering the causes and effects of the problematic factors. Results indicated that the most critical factors were respectively identified as the disregard for geomechanical changes, the lack of accurate drilling supervision and management, and the insufficient knowledge of blasting teams.

Keywords

Drilling and blasting Fuzzy cognitive map Problematic factors Tunneling 

Notes

Acknowledgements

The authors would like to thank the experts of the tunneling companies helped us during our research.

References

  1. 1.
    Murthy VMSR., Dey K, Raitani R (2003) Prediction of over break in underground tunnel blasting a case study. J Can Tunn:109–115Google Scholar
  2. 2.
    Rodriguez R, Torano J, Mene´ndez M (2007) Prediction of the airblast wave effects near a tunnel advanced by drilling and blasting. Tunn Undergr Space Technol 22:241–251CrossRefGoogle Scholar
  3. 3.
    Wei XY, Zhao ZY, Gu J (2009) Numerical simulations of rock mass damage induced by underground explosion. Int J Rock Mech Min Sci 46:1206–1213CrossRefGoogle Scholar
  4. 4.
    Rodriguez R, Lombardía C, Torno S (2010) Prediction of the air wave due to blasting inside tunnels: Approximation to a ‘phonometric curve’. Tunn Undergr Space Technol 25:483–489CrossRefGoogle Scholar
  5. 5.
    Haibo L, Xiang X, Jianchun L, Jian Z, Bo L, Yaqun L (2011) Rock damage control in bedrock blasting excavation for a nuclear power plant. Int J Rock Mech Min Sci 48:210–218CrossRefGoogle Scholar
  6. 6.
    Barton N (2012) Reducing risk in long deep tunnels by using TBM and drill-and-blast methods in the same project–the hybrid solution. J Rock Mech Geotech Eng 4(2):115–126CrossRefGoogle Scholar
  7. 7.
    Dey K, Murthy VMSR. (2012) Prediction of blast-induced overbreak from uncontrolled burn-cut blasting in tunnels driven through medium rock class. Tunn Undergr Space Technol 28:49–56CrossRefGoogle Scholar
  8. 8.
    Xia X, Li HB, Li JC, Liu B, Yu C (2013) A case study on rock damage prediction and control method for underground tunnels subjected to adjacent excavation blasting. Tunn Undergr Space Technol 35:1–7CrossRefGoogle Scholar
  9. 9.
    Cardu M, Seccatore J (2016) Quantifying the difficulty of tunnelling by drilling and blasting. Tunn Undergr Space Technol 60:178–182CrossRefGoogle Scholar
  10. 10.
    Cheng M, Prayogo D (2017) A novel fuzzy adaptive teaching–learning‑based optimization (FATLBO) for solving structural optimization problems. Eng Comput 33(1):55–69CrossRefGoogle Scholar
  11. 11.
    Hasanipanah M, Bagheri Golzar S, Abbasi Larki I, Yazdanpanah Maryaki M, · Ghahremanians T (2017) Estimation of blast-induced ground vibration through a soft computing framework. Eng Comput 33(4):951–959CrossRefGoogle Scholar
  12. 12.
    Mottahedi A, Sereshki F, Ataei M (2017) Development of overbreak prediction models in drill and blast tunneling using soft computing methods. Eng Comput.  https://doi.org/10.1007/s00366-017-0520-3 Google Scholar
  13. 13.
    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 32(1):123–133CrossRefGoogle Scholar
  14. 14.
    Bakhtavar E, Khoshrou H, Badroddin M (2015) Using dimensional-regression analysis to predict the mean particle size of fragmentation by blasting at the Sungun copper mine. Arab J Geosci 8:2111–2120CrossRefGoogle Scholar
  15. 15.
    Hasanipanah M, Jahed Armaghani D, Monjezi M, Shams S (2016) Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environ Earth Sci 75:808–819CrossRefGoogle Scholar
  16. 16.
    Taheri K, Hasanipanah M, Bagheri Golzar S, Abd Majid MZ (2017) A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Eng Comput 33(3):689–700CrossRefGoogle Scholar
  17. 17.
    Hasanipanah M, Shahnazar A, Bakhshandeh Amnieh H, Jahed Armaghani D (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Eng Comput 33(1):23–31CrossRefGoogle Scholar
  18. 18.
    Amiri M, Bakhshandeh Amineh 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 32(4):631–644CrossRefGoogle Scholar
  19. 19.
    Fouladgar N, Hasanipanah M, Bakhshandeh Amnieh H (2017) Application of cuckoo search algorithm to estimate peak particle velocity in mine blasting. Eng Comput 33(2):181–189CrossRefGoogle Scholar
  20. 20.
    Abdollahisharif J, Bakhtavar E, Nourizadeh H (2016) Green biocompatible approach to reduce the toxic gases and dust caused by the blasting in surface mining. Environ Earth Sci 75:191–203CrossRefGoogle Scholar
  21. 21.
    Bakhtavar E, Abdollahisharif J, Ahmadi M (2017) Reduction of the undesirable bench-blasting consequences with emphasis on ground vibration using a developed multi-objective stochastic programming. Int J Min Reclam Environ 31(5):333–345CrossRefGoogle Scholar
  22. 22.
    Bakhtavar E, Nourizadeh H, Sahebi AA (2017) Toward predicting blast-induced flyrock: a hybrid dimensional analysis fuzzy inference system. Int J Environ Sci Technol 14:717–728CrossRefGoogle Scholar
  23. 23.
    Axelrod R (1976) Structure of decision: the cognitive maps of political elites. Princeton University Press, PrincetonGoogle Scholar
  24. 24.
    Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24:65–75CrossRefzbMATHGoogle Scholar
  25. 25.
    Rodriguez-Repiso L, Setchi R, Salmeron JL (2007) Modelling IT projects success with fuzzy cognitive maps. Expert Syst Appl 32(2):543–559CrossRefGoogle Scholar
  26. 26.
    Schneider M, Shnaider E, Kandel A, Chew G (1998) Automatic construction of FCMs. Fuzzy Sets Syst 93:161–172CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mining and Materials EngineeringUrmia University of TechnologyUrmiaIran

Personalised recommendations