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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
  • 73 Downloads

Abstract

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.

Keywords

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

Notes

Acknowledgements

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

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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

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