PURPOSE: The present study was aimed to analyze human and organizational factors involved in mining accidents and determine the relationships among these factors.
MATERIALS AND METHODS: In this study, Human Factors Analysis and Classification System (HFACS) with Bayesian network (BN) were combined in order to analyze contributing factors in mining accidents. BN was constructed based on a hierarchal structure of HFACS. The required data were collected from a total of 295 cases of Iranian mining accidents and analyzed using HFACS. Afterwards, prior probability of contributing factors was computed using the expectation-maximization algorithm. Sensitivity analysis was applied to determine which contributing factor had a higher influence on unsafe acts to select the best intervention strategy.
RESULTS: The analyses showed that skill based errors, routine violations, environmental factors, and planned inappropriate operation had a higher relative importance in the accidents. Moreover, sensitivity analysis revealed that environmental factors, failed to correct known problem, and personnel factors had a higher influence on unsafe acts.
CONCLUSION: The results of the present study could provide guidance to help safety and health management by adopting proper intervention strategies to reduce mining accidents.
Language: en
Keywords
Bayesian network; Human Factors Analysis and Classification System; human error; mining accident prevention