Natural Hazards

, Volume 97, Issue 3, pp 1099–1113 | Cite as

Geotechnical risk evaluation of tunneling projects using optimization techniques (case study: the second part of Emamzade Hashem tunnel)

  • Reza Mikaeil
  • Sina Shaffiee HaghshenasEmail author
  • Zoheir Sedaghati
Original Paper


Tunneling projects are generally complex projects with numerous affective factors, including variable and unreliable conditions of the land. One of the appropriate tools for conducting a successful project is the implementation of risk management during its lifetime. The clustering of tunneling risks is an effective part of risk management. The research aims to achieve an optimization risk assessment based on clustering techniques in the projects which are faced with deep drillings. Hence, in this study, with contribution of the field study and use of failure modes and effects analysis results, the seven geological sections in the path of the second part of Emamzade Hashem tunnel are considered. In these seven sections, the area of instability around the tunnel, groundwater inflows and squeezing are used in the risk assessment as analysis criteria. The clustering of risks is determined by meta-heuristic algorithms such as particle swarm optimization based on stochastic optimization technique and Fuzzy C-means clustering approach as optimization techniques. The Emamzade Hashem tunnel is located in the north of Iran. The present study in the second part of Emamzade Hashem tunnel on Haraz road, one of the longest road tunneling projects in Iran, shows that results are in full compliance with soft computing results. It was found that the performance of the intelligent modelings had significant capability to evaluate the geotechnical risks of tunneling. Finally, seven sections in the path of the second part of this tunneling project were classified into two categories of the highest level and the lowest level of risk.


Tunneling risks Risk management Meta-heuristic algorithms Particle swarm optimization (PSO) Stochastic optimization Fuzzy C-means 



We would like to express our deepest thanks to Professor Mahdi Ghaem for his excellent advice.


  1. Abraham A, Guo H, Liu H (2006) Swarm intelligence: foundations, perspectives and applications. Springer, Berlin, pp 3–25Google Scholar
  2. Aryafar A, Mikaeil R, Haghshenas SS, Haghshenas SS (2018) Application of metaheuristic algorithms to optimal clustering of sawing machine vibration. Measurement. Google Scholar
  3. Aryafar A, Mikaeil R, Doulati Ardejani F, Shaffiee Haghshenas S, Jafarpour A (2019) Application of non-linear regression and soft computing techniques for modeling process of pollutant adsorption from industrial wastewaters. J Min Environ 10(2):327–337Google Scholar
  4. Assareh E, Behrang MA, Assari MR, Ghanbarzadeh A (2010) Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran. Energy 35(12):5223–5229Google Scholar
  5. Babazadeh A, Poorzahedy H, Nikoosokhan S (2011) Application of particle swarm optimization to transportation network design problem. J King Saud Univ-Sci 23(3):293–300Google Scholar
  6. Bai Q (2010) Analysis of particle swarm optimization algorithm. Comput Inf Sci 3(1):180Google Scholar
  7. Bezdek JC (1981) Objective function clustering. In: Pattern recognition with fuzzy objective function algorithms. Springer, Boston, MA, pp 43–93Google Scholar
  8. Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203Google Scholar
  9. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73Google Scholar
  10. Dormishi A, Ataei M, Kakaie RK, Mikaeil R, Haghshenas SS (2019) Performance evaluation of gang saw using hybrid ANFIS-DE and hybrid ANFIS-PSO algorithms. J Min Environ 10(2):543–557Google Scholar
  11. Eskesen SD, Tengborg P, Kampmann J, Veicherts TH (2004) Guidelines for tunnelling risk management: international tunnelling association, working group No. 2. Tunn Undergr Space Technol 19(3):217–237Google Scholar
  12. Farret R, Gombert P, Lahaie F, Cherkaoui A, Lafortune S, Roux P (2011) Design of fault trees as a practical method for risk analysis of CCS: application to the different life stages of deep aquifer storage, combining long-term and short-term issues. Energy Procedia 4:4193–4198Google Scholar
  13. Fouladgar M, Chamzini A, Basiri M (2011) Risk evaluation of tunneling projects by fuzzy TOPSIS. In: International conference on managementGoogle Scholar
  14. Haghshenas SS, Haghshenas SS, Barmal M, Farzan N (2016) Utilization of soft computing for risk assessment of a tunneling project using geological units. Civ Eng J 2(7):358–364Google Scholar
  15. Haghshenas SS, Ozcelik Y, Haghshenas SS, Mikaeil R, Moghadam PS (2017) Ranking and assessment of tunneling projects risks using fuzzy MCDM (Case study: Toyserkan doolayi tunnel). In: IMCET 2017: new trends in mining—proceedings of 25th international mining congress of Turkey. pp. 289–296Google Scholar
  16. Haghshenas SS, Mikaeil R, Haghshenas SS, Naghadehi MZ, Moghadam PS (2017b) Fuzzy and classical MCDM techniques to rank the slope stabilization methods in a rock-fill reservoir dam. Civ Eng J 3(6):382–394Google Scholar
  17. Hasanipanah M, Naderi R, Kashir J, Noorani SA, Qaleh AZA (2017) Prediction of blast-produced ground vibration using particle swarm optimization. Eng Comput 33(2):173–179Google Scholar
  18. Heller S (2006) Managing industrial risk—having a tested and proven system to prevent and assess risk. J Hazard Mater 130:58–63Google Scholar
  19. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  20. Hyun KC, Min S, Choi H, Park J, Lee IM (2015) Risk analysis using fault-tree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels. Tunn Undergr Space Technol 49:121–129Google Scholar
  21. Jalilvand P, Haghshenas Shaffiee S (2013) The study stability of Toyserkan Doolayi Tunnel using reinforce shotcrete and rock bolt under static condition. In: The 23rd international mining congress and exhibition of Turkey. pp 1299–1305Google Scholar
  22. Jalilvand P, Haghshenas Shaffiee S, Haghshenas Shaffiee S, Javan MH (2014) Evaluation of dynamic resistance of the Toyserkan Doolayi Tunnel by rock bolt and reinforced shotcrete composite system. Tunn Undergr Constr. Google Scholar
  23. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, 1942–1948. IEEE Press, New JerseyGoogle Scholar
  24. Khandelwal M, Marto A, Fatemi SA, Ghoroqi M, Armaghani DJ, Singh TN, Tabrizi O (2017) Implementing an ANN model optimized by genetic algorithm for estimating cohesion of limestone samples. Eng Comput 34:1–11Google Scholar
  25. Khanlari GR (2011) Princples of rock mechanics (in Persian). Bu-Ali Sina University Press, Hamadan. ISBN 978-600-128-031-3Google Scholar
  26. Khoury GA (2005) EU tunnel safety update. T T Int 37(2):41–43Google Scholar
  27. Koopialipoor M, Armaghani DJ, Haghighi M, Ghaleini EN (2017) A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels. Bull Eng Geol Environ 78:1–10Google Scholar
  28. Koorepazan Dezfuli A (2008) Principles of fuzzy set theory and its applications in the modeling of water engineering problems, 2nd edn. Jahad Daneshgahi, Branch of Amirkabir Industrial, TehranGoogle Scholar
  29. Li SC, Zhou ZQ, Li LP, Xu ZH, Zhang QQ, Shi SS (2013) Risk assessment of water inrush in karst tunnels based on attribute synthetic evaluation system. Tunn Undergr Space Technol 38:50–58Google Scholar
  30. Lindhe A, Rosén L, Norberg T, Bergstedt O (2009) Fault tree analysis for integrated and probabilistic risk analysis of drinking water systems. Water Res 43(6):1641–1653Google Scholar
  31. Linkov I (2006) From comparative risk assessment to multi criteria decision analysis and adaptive management: recent developments and application. Environ Int 32:1072–1093Google Scholar
  32. Lloyd SP (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–137Google Scholar
  33. Meloy A (2006) Arenal-type pyroclastic flows: a probabilistic event tree risk analysis. J Volcanol Geotherm Res 157:12–134Google Scholar
  34. Mikaeil R, Shaffiee Haghshenas S, Ozcelik Y, Shaffiee Haghshenas S (2017) Development of intelligent systems to predict diamond wire saw performance. Soft Comput Civ Eng 1(2):52–69Google Scholar
  35. Mikaeil R, Haghshenas SS, Haghshenas SS, Ataei M (2018) Performance prediction of circular saw machine using imperialist competitive algorithm and fuzzy clustering technique. Neural Comput Appl 29(6):283–292. Google Scholar
  36. Mohamad ET, Faradonbeh RS, Armaghani DJ, Monjezi M, Majid MZA (2017) An optimized ANN model based on genetic algorithm for predicting ripping production. Neural Comput Appl 28(1):393–406Google Scholar
  37. Nezarat H, Sereshki F, Ataei M (2015) Ranking of geological risks in mechanized tunneling by using fuzzy analytical hierarchy process (FAHP). Tunn Undergr Space Technol 50:358–364Google Scholar
  38. Onwunalu JE, Durlofsky LJ (2010) Application of a particle swarm optimization algorithm for determining optimum well location and type. Comput Geosci 14(1):183–198Google Scholar
  39. Rad MY, Haghshenas SS, Kanafi PR, Haghshenas SS (2012) Analysis of protection of body slope in the rockfill reservoir dams on the basis of fuzzy logic. In: IJCCI. pp 367–373Google Scholar
  40. Rad MY, Haghshenas SS, Haghshenas SS (2014) Mechanostratigraphy of cretaceous rocks by fuzzy logic in East Arak, Iran. In: The 4th international workshop on computer science and engineering-summer, WCSEGoogle Scholar
  41. Salemi A., Mikaeil R., Haghshenas, SS (2018) Integration of finite difference method and genetic algorithm to seismic analysis of circular shallow tunnels (Case study: Tabriz urban railway tunnels). KSCE J Civ Eng 22(5):1978–1990. Google Scholar
  42. Sayadi A, Rajabzadeh A, Hosseinpor M, Hayati M (2009) Risk ranking in tunneling projects using TOPSIS method. In: 8th Iranian tunneling conference. TehranGoogle Scholar
  43. Storn R, Price K (1995) Differential evolution- a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, International Computer Science Institute, Berkeley, CAGoogle Scholar
  44. Storn R, Price K (1996) Minimizing the real functions of the ICEC’96 contest by differential evolution. In: Proceedings of IEEE international conference on evolutionary computation, 1996. IEEE, pp 842–844Google Scholar
  45. Ünler A (2008) Improvement of energy demand forecasts using swarm intelligence: the case of Turkey with projections to 2025. Energy Policy 36(6):1937–1944Google Scholar
  46. Vaurio JK (2010) Ideas and developments in importance measures and fault-tree techniques for reliability and risk analysis. Reliab Eng Syst Saf 95(2):99–107Google Scholar
  47. Vílchez J, Espenjo V, Casal J (2011) Generic event trees and probabilities for the release of different types of hazardous materials. J Loss Prevent Process Ind 24:281–287Google Scholar
  48. Vutukuri VS, Katsuyama K (1994) Introduction to rock mechanics. National Institute for Resources and Environment, Japan Industrial Publication and Consulting Inc., TokyoGoogle Scholar
  49. Wang T, Lee H (2008) Developing a fuzzy TOPSIS approach based on subjective weight and objective weight. Exp. Syst. Appl 36:8980–8985Google Scholar
  50. Yu S, Zhu K, Zhang X (2012) Energy demand projection of China using a path-coefficient analysis and PSO–GA approach. Energy Convers Manag 53(1):142–153Google Scholar
  51. Zadeh Lotfi A (1965) Fuzzy sets. Inf Control 8(3):338–353Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Faculty of Mining and Metallurgical EngineeringUrmia University of TechnologyUrmiaIran
  2. 2.Young Researchers and Elite Club, Rasht BranchIslamic Azad UniversityRashtIran

Personalised recommendations