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

Author

Listed:
  • Reza Mikaeil

    (Urmia University of Technology)

  • Sina Shaffiee Haghshenas

    (Islamic Azad University)

  • Zoheir Sedaghati

    (Urmia University of Technology)

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.

Suggested Citation

  • Reza Mikaeil & Sina Shaffiee Haghshenas & Zoheir Sedaghati, 2019. "Geotechnical risk evaluation of tunneling projects using optimization techniques (case study: the second part of Emamzade Hashem tunnel)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(3), pages 1099-1113, July.
    • Handle: RePEc:spr:nathaz:v:97:y:2019:i:3:d:10.1007_s11069-019-03688-z
    DOI: 10.1007/s11069-019-03688-z
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    Cited by:

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    4. Xiaojie Geng & Shunchuan Wu & Yanjie Zhang & Junlong Sun & Haiyong Cheng & Zhongxin Zhang & Shijiang Pu, 2023. "Developing hybrid XGBoost model integrated with entropy weight and Bayesian optimization for predicting tunnel squeezing intensity," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 119(1), pages 751-771, October.
    5. Behrouz Pirouz & Sina Shaffiee Haghshenas & Sami Shaffiee Haghshenas & Patrizia Piro, 2020. "Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligenc," Sustainability, MDPI, vol. 12(6), pages 1-21, March.
    6. Giuseppe Guido & Sina Shaffiee Haghshenas & Sami Shaffiee Haghshenas & Alessandro Vitale & Vincenzo Gallelli & Vittorio Astarita, 2020. "Development of a Binary Classification Model to Assess Safety in Transportation Systems Using GMDH-Type Neural Network Algorithm," Sustainability, MDPI, vol. 12(17), pages 1-19, August.
    7. Jui-Sheng Chou & Dinh-Nhat Truong & Yonatan Che, 2020. "Optimized multi-output machine learning system for engineering informatics in assessing natural hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 101(3), pages 727-754, April.
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