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Risk assessment of unsafe behavior in university laboratories using the HFACS-UL and a fuzzy Bayesian network

机译:Risk assessment of unsafe behavior in university laboratories using the HFACS-UL and a fuzzy Bayesian network

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Introduction: Risk assessment for unsafe behavior is an important task in the management of university laboratories. Yet related research activities are still in the early stages. This paper attempts to deepen the insight and provide a basis for further research. Method: As traditional methods are inadequate in terms of quantitative assessment and uncertainty handling, this paper proposes a method to assess the risk of unsafe behavior in university laboratories using the human factor analysis and classification system for university laboratories (HFACS-UL)-fuzzy Bayesian network (BN) approach. A BN structure was estab-lished using the HFACS-UL model for the identification of factors influencing unsafe behavior. Using a fuzzy BN approach, parameters are learned based on prior knowledge and expert experience. The model is then applied for inference analysis to identify the main risk factors. The key agents were also analyzed along with meta-networks to determine further preventive and control measures. Results: Taking chem-istry laboratories of a university as an example, the results show that the probability of unacceptable unsafe behavior in chemical laboratories is 86%, indicating that commitment and cooperation from dif-ferent agents are required. Of the 24 risk factors, poor organizational climate, with a sensitivity value of 24.1%, has the greatest impact on unsafe behavior. The most fundamental factor contributing to the occurrence of unsafe behavior is inadequate legislation, which in turn results in unacceptable external factors and inadequate supervision, thus forming the most likely causal chain. The functional department, lab center director, and secondary faculty leadership team are the most critical agents. Conclusions: Results from the chemistry laboratories demonstrate the credibility of the model. Practical applications: This study may help provide technical support for risk management in university laboratories. (c) 2022 National Safety Council and Elsevier Ltd. All rights reserved.

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