An Advanced Method of Analysing Mine Accidents Using an Efficient MCDM under Fermatean Neutrosophic Fuzzy Environment

Root cause analysis of mine accidents needs further improvement using advanced hybrid models for better efficacy and future mitigations. The paper focuses on prioritizing the root causes (Basic Events, BEs) of a mining accident by calculating their contribution probabilities to the Top Event (TE) (locomotive derailment accident) and addressing the most critical factors to prevent future accidents. The hybrid approach incorporates Fault Tree Analysis (FTA) from the Bowtie method and Fermatean Neutrosophic (FN) fuzzy sets, integrating eight Multi-Criteria Decision-Making (MCDM) methods. Sensitivity analysis is used to select the most stable and consistent method for prioritizing BEs. Fussell-Vesely (FV) measures are applied to calculate the contribution probabilities of the BEs to the TE. Using the selected Measurement of Alternatives and Ranking According to The Compromise Solution (MARCOS) method within a FN fuzzy environment, it is determined that ‘Improper track design’ has the highest occurrence probability at 8.02 × 10−2, followed by ‘Poor task allocation planning’ and ‘Lack of multi-tasking’ with probabilities of 2.98 × 10−2 and 2.86 × 10−2, respectively. Meanwhile, ‘Over speeding’ scored the highest in FV measures, indicating its most significant contribution to the occurrence of the TE, followed by Improper track design and Poor task allocation planning. The model effectively identified and ranked the Root Causes, providing valuable insights for developing targeted intervention strategies to prevent future accidents. Summarily, the study demonstrated that the FTA (Bowtie)-MARCOS-FN model for accident analysis had performed better than existing analytical models.