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Decision analysis of safety risks pre-control measures for falling accidents in mega hydropower engineering driven by accident case texts

Author

Listed:
  • Cao, Kunyu
  • Chen, Shu
  • Chen, Yun
  • Nie, Benwu
  • Li, Zhi

Abstract

Falling accidents have a high probability of occurring during the construction of mega hydropower engineering (MHE), involving various risk factors. Previous studies have paid limited attention to the decision-making management of risk pre-control measures. To address this, we propose a text data-driven decision analysis method. First, we couple fault tree analysis (FTA) with grey relational analysis (GRA), developing the FTA-GRA method to assess the importance of risk factors, identify main factors, and formulate pre-control measures. Subsequently, the Cloud Model (C) and TODIM (an acronym in Portuguese for Interactive and Multi-criteria Decision Making) were used to improve Multi-Attributive Border Approximation Area Comparison (MABAC), and the C-TODIM-MABAC decision-making method was constructed to quantify the priority of each pre-control measure to select the optimal solution. The results show that (1) among the top 10 risk factors in terms of importance, workers' non-compliant operations and neglect of safety issues are particularly prominent. (2) The C-TODIM-MABAC method fully considers the fuzziness and randomness of evaluation language and the bounded rationality of decision-makers, making it more suitable for MHE construction scenarios. (3) Sensitivity analysis verifies the applicability and reliability of the method from the dual perspectives of criterion weights and evaluation scales.

Suggested Citation

  • Cao, Kunyu & Chen, Shu & Chen, Yun & Nie, Benwu & Li, Zhi, 2025. "Decision analysis of safety risks pre-control measures for falling accidents in mega hydropower engineering driven by accident case texts," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    • Handle: RePEc:eee:reensy:v:261:y:2025:i:c:s0951832025003217
    DOI: 10.1016/j.ress.2025.111120
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    4. Zhu, Enping & Li, Tao & Xiong, Jinbiao & Chai, Xiang & Zhang, Tengfei & Liu, Xiaojing, 2026. "A digital twin framework for real-time operation monitoring and its future prediction for space nuclear power," Reliability Engineering and System Safety, Elsevier, vol. 267(PB).

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