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

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.