Development of Near miss Assessment Model Using Bayesian Network and Derivation of Major Causes 


Vol. 38,  No. 4, pp. 54-59, Aug.  2023
10.14346/JKOSOS.2023.38.4.54


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  Abstract

ddition, a sensitivity analysis was conducted to derive the major factors. To verify the validity of the model, near-miss data obtained from the ethylene production process were applied. As a result, “PE2 (education),” “PR1 (procedure),” and “TE1 (equipment and facility not installed)” were derived as the major factors causing near misses in this process. If actual workplace data are applied to the near-miss assessment model developed in this study, results that are unique to the workplace can be confirmed. In addition, scientific safety management is possible only when priority is given through sensitivity analysis.

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[IEEE Style]

하선영, 이미정, 백종배, "Development of Near miss Assessment Model Using Bayesian Network and Derivation of Major Causes," Journal of the Korean Society of Safety, vol. 38, no. 4, pp. 54-59, 2023. DOI: 10.14346/JKOSOS.2023.38.4.54.

[ACM Style]

하선영, 이미정, and 백종배. 2023. Development of Near miss Assessment Model Using Bayesian Network and Derivation of Major Causes. Journal of the Korean Society of Safety, 38, 4, (2023), 54-59. DOI: 10.14346/JKOSOS.2023.38.4.54.

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