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Novel model for risk identification during karst excavation

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  • Lin, Song-Shun
  • Shen, Shui-Long
  • Zhou, Annan
  • Xu, Ye-Shuang

Abstract

This study proposes a novel fuzzy model for identifying high-risk factors during excavations in urban karst geological environments. The proposed model incorporates the interval analytic hierarchy process (I-AHP) into the technique for order preference by similarity to an ideal solution (TOPSIS). The developed model considers the complex geological conditions, monitoring data, management quality, and surrounding environment. I-AHP is employed to assign the weights of the criteria, while TOPSIS is applied to identify high-risk factors. An expert confidence index is introduced to increase the reliability of the evaluated results. A case study of karst excavation at Ma'anshan Park Station on Guangzhou Metro Line 9 is analysed to validate the proposed model. The results indicate that high-risk factors can be identified in different excavation stages. The evaluated results concerning variations in risk factors can provide a guide for adopting construction measures to mitigate risk and reduce accident occurrence.

Suggested Citation

  • Lin, Song-Shun & Shen, Shui-Long & Zhou, Annan & Xu, Ye-Shuang, 2021. "Novel model for risk identification during karst excavation," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:reensy:v:209:y:2021:i:c:s0951832021000077
    DOI: 10.1016/j.ress.2021.107435
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    References listed on IDEAS

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    1. Wu, Xianguo & Liu, Huitao & Zhang, Limao & Skibniewski, Miroslaw J. & Deng, Qianli & Teng, Jiaying, 2015. "A dynamic Bayesian network based approach to safety decision support in tunnel construction," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 157-168.
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    5. Sherong Zhang & Bo Sun & Lei Yan & Chao Wang, 2013. "Risk identification on hydropower project using the IAHP and extension of TOPSIS methods under interval-valued fuzzy environment," 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. 65(1), pages 359-373, January.
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    Cited by:

    1. Li, Bo & Zhang, Qiling & Yang, Shengmei & Tian, Yaling & Li, Zhi, 2023. "Identification of failure modes and paths of reservoir dams under explosion loads," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    2. Bo Wu & Yu Wei & Guowang Meng & Shixiang Xu & Qinshan Wang & Dianbin Cao & Chenxu Zhao, 2023. "Multi-Source Monitoring Data Fusion Comprehensive Evaluation Method for the Safety Status of Deep Foundation Pit," Sustainability, MDPI, vol. 15(15), pages 1-18, August.
    3. Lingyun, Guo & Markus, Niffenegger & Jing, Zhou, 2022. "A novel procedure to evaluate the performance of failure assessment models," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Shen, Shui-Long & Lin, Song-Shun & Zhou, Annan, 2023. "A cloud model-based approach for risk analysis of excavation system," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    5. Yuanmin Wang & Mingkang Yuan & Xiaofeng Zhou & Xiaobing Qu, 2023. "Evaluation of Geo-Environment Carrying Capacity Based on Intuitionistic Fuzzy TOPSIS Method: A Case Study of China," Sustainability, MDPI, vol. 15(10), pages 1-21, May.

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