ANALYSIS OF COLLISION ACCIDENTS IN MARITIME TRANSPORTATION BY FTA METHOD

Abstract

ANALYSIS OF COLLISION ACCIDENTS IN MARITIME TRANSPORTATION BY FTA METHOD

Abstract

The aim of this study is to determine the possible causes of collision accidents and identify the contribution of possibility on top events by using Fault Tree Analysis (FTA). A total of 62 collision accidents were considered between 2005 and 2020 and detailed technical data on marine accidents were provided from accident reports obtained by Marine Accident Investigation Branch (MAIB). The study found that most of the factors (E1/Misuse of navigational tools, E3/COLREG Rule-5 (Look-out)) that had the greatest effect on the collision were mainly due to the inadequacy to keep a safe navigation watch. For that reason, the findings of the study are very important in terms of determining the strategies to eliminate the risks for future accident prevention. For further research, it is recommended that consideration be given to a longer data period, including other navigational areas. In addition, alternative risk assessment methods should be applied considering other types of vessels for better comparisons.

Keywords

• Collision
• Marine Accident
• Fault Tree Analysis
• Root Cause Analysis

References

1. Allianz Global Corporate & Specialty, (AGCS). Safety and Shipping Review 2019 Report. 03.03.2021, https://www.agcs.allianz.com/content/dam/onemarketin g/agcs/agcs/reports/AGCS-Safety-Shipping-Review-2020.pdf.
2. Antao, P., Soares, C. G. (2006). Fault-tree models of accident scenarios of RoPax vessels. International Journal of Automation and Computing, 3(2): 107-116.
3. Arslan, Ö., Zorba, Y., Svetak, J. (2018). Fault Tree Analysis of Tanker Accidents during Loading and Unloading Operations at the Tanker Terminals. Journal of ETA Maritime Science, 6(1), 3-16.
4. Chang, S. E., Stone, J., Demes, K., Piscitelli, M. (2014). Consequences of oil spills: a review and framework for informing planning. Ecology and Society, 19(2). doi:10.5751/es-06406-190226
5. Chen, J., Bian, W., Wan, Z., Yang, Z., Zheng, H., Wang, P. (2019). Identifying factors influencing total-loss marine accidents in the world: Analysis and evaluation based on ship types and sea regions. Ocean Engineering, 191, 106495.
6. Chen, P., Huang, Y., Mou, J., Van Gelder, P. H. A. J. M. (2019). Probabilistic risk analysis for ship-ship collision: State-of-the-art. Safety Science, 117, 108-122.
7. Chen, P., Mou, J., Li, Y. (2015). Risk analysis of maritime accidents in an estuary: a case study of Shenzhen Waters. Zeszyty Naukowe/Akademia Morska w Szczecinie, 42, 114, 54-62.
8. De Maya, B. N., Kurt, R. E. (2020). Marine Accident Learning with Fuzzy Cognitive Maps (MALFCMs): A case study on bulk carrier's accident contributors. Ocean Engineering, 208: 107197

9. De Maya, B. N., Babaleye, A. O., Kurt, R. E. (2020). Marine accident learning with fuzzy cognitive maps (MALFCMs) and Bayesian networks. Safety in Extreme Environments, 2(1), 69-78.
10. Du, L., Goerlandt, F., Kujala, P. (2020). Review and analysis of methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data. Reliability Engineering & System Safety, 200, 106933.
11. Eliopoulou, E., Hamann, R., Papanikolaou, A., Golyshev, P. (2013). Casualty analysis of cellular container ships. Proceedings of the IDFS, 2013(Shanghai), 25-27.
12. Eliopoulou, E., Papanikolaou, A., Voulgarellis, M. (2016). Statistical analysis of ship accidents and review of safety level. Safety Science, 85: 282-292.
13. European Maritime Safety Agency (EMSA), (2020). Preliminary Annual Overview of Marine Casualties and Incidents 2014-2019 reports. 17.04.2021, http://www.emsa.europa.eu/emsa documents/latest/tagged/85-annual-overview.html.
14. Fan, S., Yang, Z., Blanco-Davis, E., Zhang, J., Yan, X. (2020). Analysis of maritime transport accidents using Bayesian networks. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 1748006X1990085. doi:10.1177/1748006x19900850
15. Guan, Y., Zhao, J., Shi, T., Zhu, P. (2016). Fault tree analysis of fire and explosion accidents for dual fuel (diesel/natural gas) ship engine rooms. Journal of Marine Science and Application, 15(3): 331-335.
16. Hänninen, M., Kujala, P. (2012). Influences of variables on ship collision probability in a Bayesian belief network model. Reliability Engineering & System Safety, 102: 27-40.
17. International Maritime Organization (IMO). (2019). Maritime Safety. 12.03.2021,www.imo.org/en/OurWork/Safety/Pages/Default.aspx
18. Japan Transport Safety Board (JTSB). Marine accident database. 03.04.2021,https://www.mlit.go.jp/jtsb/statistics_mar.html.
19. Kececi, T., Arslan, O. (2017). SHARE technique: A novel approach to root cause analysis of ship accidents. Safety Science, 96: 1-21.
20. Khakzad, N., Khan, F., Amyotte, P. (2011). Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches. Reliability Engineering & System Safety, 96(8): 925-932.
21. Kum, S., Sahin, B. (2015). A root cause analysis for Arctic Marine accidents from 1993 to 2011. Safety Science, 74, 206–220. doi:10.1016/j.ssci.2014.12.010
22. Lu, C. S., Tsai, C. L. (2008). The effects of safety climate on vessel accidents in the container shipping context. Accident Analysis & Prevention, 40(2): 594-601.
23. Luo, M., Shin, S. H. (2019). Half-century research developments in maritime accidents: Future directions. Accident Analysis & Prevention, 123, 448-460
24. Papanikolaou, A., Eliopoulou, E., Alissafaki, A., Mikelis, N., Aksu, S., Delautre, S. (2007). Casualty analysis of Aframax tankers. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 221(2): 47-60.
25. Puisa, R., Lin, L., Bolbot, V.,Vassalos, D. (2018). Unravelling causal factors of maritime incidents and accidents. Safety Science, 110: 124-141.
26. Ruijters, E., Stoelinga, M. (2015). Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools. Computer Science Review, 15: 29-62
27. Senol, Y. E., Aydogdu, Y. V., Sahin, B., Kilic, I. (2015). Fault Tree Analysis of chemical cargo contamination by using fuzzy approach. Expert Systems with Applications, 42(12), 5232–5244. doi:10.1016/j.eswa.2015.02.027
28. Ugurlu, Ö. (2011). Petrol Tankerlerinde Meydana Gelen Deniz Kazalarının Risk Analizi. (Doktora Tezi). Trabzon: Karadeniz Teknik  Üniversitesi Fen Bilimleri Enstitüsü.
29. Ugurlu, O., Kose, E., Yıldırım, U.,Yüksekyıldız, E. (2015). Marine accident analysis for collision and grounding in oil tanker using FTA method. Maritime Policy & Management, 42(2): 163-185.
30. United Nations Conference on Trade and Development (UNCTAD), (2019). Review of Maritime Transport. 12.02.2021,https://unctad.org/system/files/official-document/rmt2019_en.pdf.
31. Unver, B., Gürgen, S., Sahin, B., Altın, İ. (2019). Crankcase explosion for two-stroke marine diesel engine by using fault tree analysis method in fuzzy environment. Engineering Failure Analysis, 97, 288–299. doi:10.1016/j.engfailanal.2019.01.007
32. Wang, H., Liu, Z., Wang, X., Graham, T.,Wang, J. (2021). An analysis of factors affecting the severity of marine accidents. Reliability Engineering & System Safety, 210: 107513.
33. Wang, L., Yang, Z. (2018). Bayesian network modelling and analysis of accident severity in waterborne transportation: A case study in China. Reliability Engineering & System Safety, 180:277-289.
34. Yip, T. L., Jin, D., Talley, W. K. (2015). Determinants of injuries in passenger vessel accidents. Accident Analysis & Prevention, 82: 112-117.
35. Zhang, S., Pedersen, P. T., Villavicencio, R. (2019). Probability of ship collision and grounding. Probability and Mechanics of Ship Collision and Grounding, 1–61. doi:10.1016/b978-0-12-815022-1.00001-3.
36. Zhang, Y., Sun, X., Chen, J., Cheng, C. (2021). Spatial patterns and characteristics of global maritime accidents. Reliability Engineering & System Safety, 206: 107310.
37. Zhou, T., Wu, C., Zhang, J., Zhang, D. (2017). Incorporating CREAM and MCS into fault tree analysis of LNG carrier spill accidents. Safety Science, 96: 183-191.