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Novel model for risk identification during karst excavation

Author

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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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    Cited by:

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    3. Zhang, Pei & Zhang, Zhen-Ji & Gong, Da-Qing, 2024. "An improved failure mode and effect analysis method for group decision-making in utility tunnels construction project risk evaluation," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
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    6. 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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