Journal of the Korean Society of Safety (한국안전학회지)

The Korean Society of Safety (한국안전학회)
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Development of Near miss Assessment Model Using Bayesian Network and Derivation of Major Causes

베이지안 네트워크를 이용한 아차사고 평가 모델 개발 및 주요 원인 도출

  • Seon Yeong Ha (Department of Safety Engineering, Korea National University of Transportation) ;
  • Mi Jeong Lee (Department of Safety Engineering, Korea National University of Transportation) ;
  • Jong-Bae Baek (Department of Safety Engineering, Korea National University of Transportation)
  • 하선영 (한국교통대학교 안전공학과) ;
  • 이미정 (한국교통대학교 안전공학과) ;
  • 백종배 (한국교통대학교 안전공학과)
  • Received : 2023.05.26
  • Accepted : 2023.08.21
  • Published : 2023.08.31
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Abstract

The relationship between near misses and major accidents can be confirmed using the ratios proposed by Heinrich and Bird. Systematic reviews of previous national and international studies did not reveal the assessment process used in near-miss management systems. In this study, a model was developed for assessing near misses and major factors were derived through case application. By reviewing national and international literature, 14 factors were selected for each dimension of the P2T (people, procedure, technology) model. To identify the causal relationship between accidents and these factors, a near-miss assessment model was developed using a Bayesian network. In addition, 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.

Keywords

Acknowledgement

This work was supported by 2023 Graduate School of Chemical Safety Management Specialization funded by the Ministry of Environment.

References

  1. EcoOnline, "Near Miss & Incident Reporting: Everything You Need to Know", https://www.ecoonline.com/-incidentreporting-everything-you-need-to-know#practical-measur es-for-near-miss-reporting
  2. Occupational Safety and Health Administration, "Incident Investigation", https://www.osha.gov/incident-investigation
  3. H.W.Heinrich, "Industrial Accident Prevention", Fifth Edition, McGRAW-HILL BOOK COMPANY, 1980.
  4. Ministry of Employment and Labor, Guidebook for building safety and health management, pp.42-43, 2022.
  5. M.G. Gnoni et al., "Near Miss Management Systems in the Industrial Sector: A Literature Review", Safety Science, Vol. 150, 2022.
  6. Simon Jones et al., "The Importance of Near Miss Reporting to Further Improve Safety Performance", Journal of Loss Prevention in the Process Industries, Vol. 12, Issue 1, pp. 59-67, 1999. https://doi.org/10.1016/S0950-4230(98)00038-2
  7. H. J. Yoon et al., "Development of a Near Miss Management System to Prevent Industrial Accident", Journal of the Korean Institute of Chemical Engineers, Vol. 38, No. 2, pp. 166-172, 2000.
  8. C. H. Lee, "Relationship between Company Safety Program and Safety Culture, Safety Accident(incl. near misses) : Focus on the Maintenance and Construction Work's Safety in Natural Gas Facilities, Chung-Ang University, Master's Thesis, 2018.
  9. S. Cavalieri and W.M. Ghislandi, "A Conceptual Structure for the use of Near Misses Properties", IFAC Proceedings Volumes, Vol. 39, Issue 3, pp. 81-86, 2006. https://doi.org/10.3182/20060517-3-FR-2903.00048
  10. M. G. Gnoni and Gianni Lettera, "Near Miss Management Systems: A Methodological Comparison", Journal of Loss Prevention in the Process Industries, Vol. 25, Issue 3, pp. 609-616, 2012. https://doi.org/10.1016/j.jlp.2012.01.005
  11. J. H. Lee, "The Effect of Finding Near Miss from the Construction Site in Accidents", Seoul National University of Science and Technology, Master's Thesis, 2015.
  12. G. N. Min, "Comparison of Types of Near Miss among Construction Workers in Mid-low, High and Super High-rise Building Sites", Korea University, Master's Thesis, 2016.
  13. S. G. Lee, "Study on the Correlation between Manufacturing Near-niss Reporting Activities and Safety Culture Recognition", Korea National University of Transportation, Master's Thesis, 2020.
  14. S. G. Lee and J. C. Park, "A Study on the Status and the Perception of Near Miss Reporting Activities in Domestic Manufacturing Industry", Journal of the Korea Convergence Society, Vol. 12, No. 2, pp. 287-294, 2021.
  15. D. Y. Kim and J. H. Park, "Comparison of Safety Culture Awareness between Client and Subcontractors' Employees according to the Experience of Accidents and Near Misses", J. Korean Soc. Saf., Vol. 37, No. 2, pp. 28-34, 2022.
  16. M. G. Gnoni and J. H. Saleh, "Near Miss Management Systems and Observability-in-depth: Handling Safety Incidents and Accident Precursors in Light of Safety Principles", Journal of Safety Science, Vol. 91, pp. 154-167, 2017. https://doi.org/10.1016/j.ssci.2016.08.012
  17. F. Chen et al., "Risk Assessment of Chemical Process Considering Dynamic Probability of Near Misses based on Bayesian Theory and Event Tree Analysis", Journal of Loss Prevention in the Process Industries, Vol. 68, 2020.
  18. S. W. Seo et al., "Effective Detection Technique of Near Miss using 4M Risk Assesment Methodology", J. Korean Soc. Saf., Vol. 27, No. 5, pp. 164-170, 2012.
  19. J. B. Baek, "A Study on Mechanism Analysis for Industrial Fatality Accident", Han Yang University, Master's Thesis, 1991.
  20. P. Kleindorfer et al., "Assessment of Catastrophe Risk and Potential Losses in Industry", Computers & Chemical Engineering, Vol. 47, pp. 85-96, 2012.
  21. T. K. Lim et al., "A Methodology for Measuring and Assessing Construction Worker's Near-misses", Journal of the Architectural Institute of Korea, Vol. 30, No. 1, pp. 71-79, 2014. https://doi.org/10.5659/JAIK_SC.2014.30.1.071
  22. S. J. Dee et al., "Using Near Misses to Improve Risk Management Decisions", Process Safety Progress, Vol 32, Issue 4, pp. 322-327, 2013. https://doi.org/10.1002/prs.11632
  23. A. Dagvasumberel et al., "Railroad Near-miss Occurrence Detection and Risk Estimation System with Data from Camera Using Deep Learning", Institute of Electrical and Electronics Engineering, pp. 83-87, 2021.
  24. P. Spirtes, C. Glymour and Richard Scheines, Causation, Prediction, and Search, Second Edition, Cambridge, MIT Press, pp. 11-17, 2000.
  25. J. G. Lee, R Program Recipes for Bayesian Statistics, pp. 11-19, 2018.
  26. AGENA RISK, "Bayesian Network Software for Risk Analysis and Decision Making", https://www.agenarisk.com/
  27. Occupational Safety and Health Research Institute, Investigation of Causes of Industrial Accidents (Occupational Accidents), pp. 612-619, 2014.
  28. H. S. Park and W. K. Oh, "A Test for Exogeneity of Monetary Policy in Korea : An Application of DAG", Korea Review of Applied Economics, Vol. 9, No. 3, pp. 93-118, 2007.
  29. Norman Fenton and Martin Neil, Risk Assessment and Decision Analysis with Bayesian Networks, Second Edition, CRC Press, 2018.