Research Article


DOI :10.26650/JTL.2023.1243766   IUP :10.26650/JTL.2023.1243766    Full Text (PDF)

Risk Assessment of Solid Bulk Cargo Liquefaction Consequences in Maritime Transportation under a Fuzzy Bayesian Network Approach

Muhammet Aydın

Solid bulk cargo liquefaction is hazardous for bulk carrier ships as they reduce the stability of the ship. Most dry bulk ship owners face solid bulk cargo liquefaction during the carriage of ore cargoes. The consequences of cargo liquefaction could have catastrophic effects such as the ship sinking or capsizing. To improve the process of safety during the shipment of bulk cargo and reduce potential consequences, a detailed risk analysis is needed. The purpose of this paper is to conduct a systematic probabilistic risk analysis of the liquefaction of solid bulk cargo in the marine sector in order to allay this concern in order to deal with complex causation and uncertainty resulting from complex interdependence among risk factors, limited data, and a complex environment. A Bayesian network (BN) method under fuzzy logic has been utilized in the research. Whilst the BN enables us to calculate the conditional probability of each basic event in the graph, the fuzzy logic tackles uncertainty and the vagueness of expert judgment. The findings of the paper will assist solid bulk cargo owners and shippers in reducing the risk of solid bulk cargo liquefaction during maritime transportation.


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References

  • Abimbola, M., Khan, F., & Khakzad, N. (2014). Dynamic safety risk analysis of offshore drilling. Journal of Loss Prevention in the Process Industries, 30(1), 74-85. https://doi.org/10.1016/jjlp.2014.05.002. google scholar
  • Abaei, M. M., Abbassi, R., Garaniya, V., Chai, S., & Khan, F. (2018). Reliability assessment of marine floating structures using Bayesian network. Applied Ocean Research, 76, 51-60. google scholar
  • Akyuz, E., Arslan, O., & Turan, O. (2020). Application of fuzzy logic to fault tree and event tree analysis of the risk for cargo liquefaction on board ship. Applied Ocean Research, 101:1-10. 102238. google scholar
  • Akyuz, E., & Celik, E. (2018). A quantitative risk analysis by using interval type-2 fuzzy FMEA approach: the case of oil spill. Maritime Policy and Management, 45(8), 979-994. https://doi.org/10.1080/03088839.2018.1520401. google scholar
  • Akyuz, E., Celik, E., Celik, M. (2018). A practical application of human reliability assessment for operating procedures of the emergency fire pump at ship. Ships and Offshore Structures, 13(2), 208-216. google scholar
  • Arslan, O. (2009). Quantitative evaluation of precautions on chemical tanker operations. Process Safety and Environmental Protection, 87(2),113-120. google scholar
  • Aydin, M., Arici, S. S., Akyuz, E., Arslan, O. (2021a). A probabilistic risk assessment for asphyxiation during gas inerting process in chemical tanker ship. Process Safety and Environmental Protection, 155, 532-542. google scholar
  • Aydin, M., Akyuz, E., Turan, O., & Arslan, O. (2021b). Validation of risk analysis for ship collision in narrow waters by using fuzzy Bayesian networks approach. Ocean Engineering, 231, 108973. google scholar
  • Aydın, M., & Kamal, B. (2022). A Fuzzy-Bayesian Approach on the Bankruptcy of Hanjin Shipping. Journal of ETA Maritime Science, 10(1), 2-15. google scholar
  • Aziz, A., Ahmed, S., Khan, F., Stack, C., Lind, A. (2019). Operational risk assessment model for marine vessels. Reliability Engineering and System Safety, 185(December 2018), 348-361. https://doi.org/10.1016/j.ress.2019.01.002 google scholar
  • Cem Kuzu, A., Akyuz, E., Arslan, O. (2019). Application of Fuzzy Fault Tree Analysis (FFTA) to maritime industry: A risk analysing of ship mooring operation. Ocean Engineering, 179(May 2018), 128-134. https://doi.org/10.1016/j.oceaneng.2019.03.029 google scholar
  • Cheraghi, M., Eslami Baladeh, A., Khakzad, N. (2019). A fuzzy multi-attribute HAZOP technique (FMA-HAZOP): Application to gas wellhead facilities. Safety Science, 114(December 2018), 12-22. https://doi.org/10.1016/j.ssci.2018.12.024 google scholar
  • Clemen, R. T., Winkler, R. L. (1999). Combining probability distributions from experts in risk analysis. Risk Analysis, 19(2), 187-203. https://doi.org/10.1023/A:1006917509560 google scholar
  • Cormier, R., Elliott, M., Rice, J. (2019). Putting on a bow-tie to sort out who does what and why in the complex arena of marine policy and management. Science of the Total Environment, 648, 293-305. https://doi.org/10.1016/j.scitotenv.2018.08.168 google scholar
  • Cakir, E., Sevgili, C., Fiskin, R. (2021). An analysis of severity of oil spill caused by vessel accidents. Transportation Research Part D: Transport and Environment, 90, 102662. google scholar
  • Dai, H., Chen, X., Ye, M., Song, X., Hammond, G., Hu, B., Zachara, J. M. (2019). Using Bayesian Networks for Sensitivity Analysis of Complex google scholar
  • Biogeochemical Models. Water Resources Research, 55(4), 3541-3555. https://doi.org/10.1029/2018WR023589 google scholar
  • de Melo, A. C. V., Sanchez, A. J. (2008). Software maintenance project delays prediction using Bayesian Networks. Expert Systems with Applications, 34(2), 908-919. https://doi.org/10.1016/j.eswa.2006.10.040 google scholar
  • Emovon, I., Norman, R. A., Murphy, A. J., Pazouki, K. (2015). An integrated multicriteria decision making methodology using compromise solution methods for prioritising risk of marine machinery systems. Ocean Engineering, 105, 92-103. https://doi.org/10.1016/j.oceaneng.2015.06.005 google scholar
  • EMSA. (2014). Annual Overview of Marine Casualties and Incidents 2014. google scholar
  • F. Goerlandt, J. Montewka, V. Kuzmin, P. Kujala, A risk-informed ship collision alert system: framework and application, Safety. Sci. 77 (2015) 182-204. google scholar
  • Fu, S., Zhang, D., Montewka, J., Zio, E., & Yan, X. (2016). A fuzzy event tree model for accident scenario analysis of ship stuck in ice in arctic waters. Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, 8(June). https://doi.org/10.1115/OMAE2016-54882 google scholar
  • Garre, L., & Rizzuto, E. (2012). Bayesian networks for probabilistic modelling of still water bending moment for side-damaged tankers. Ships and Offshore Structures, 7(3), 269-283. https://doi.org/10.1080/17445302.2011.590695 google scholar
  • Goerlandt, F., & Montewka, J. (2015). A framework for risk analysis of maritime transportation systems: A case study for oil spill from tankers in a ship-ship collision. Safety Science, 76, 42-66. https://doi.org/10.1016/j.ssci.2015.02.009 google scholar
  • Hanninen, M., Valdez Banda, O. A., & Kujala, P. (2014). Bayesian network model of maritime safety management. Expert Systems with Applications, 41(17), 7837-7846. https://doi.org/10.1016/j.eswa.2014.06.029 google scholar
  • John, A., Yang, Z., Riahi, R., & Wang, J. (2016). A risk assessment approach to improve the resilience of a seaport system using Bayesian networks. Ocean Engineering, 111, 136-147. https://doi.org/10.1016/j.oceaneng.2015.10.048 google scholar
  • Jones, B., Jenkinson, I., Yang, Z., & Wang, J. (2010). The use of Bayesian network modelling for maintenance planning in a manufacturing industry. Reliability Engineering & System Safety, 95(3), 267-277. google scholar
  • Kabir, G., Tesfamariam, S., Francisque, A., & Sadiq, R. (2015). Evaluating risk of water mains failure using a Bayesian belief network model. European Journal of Operational Research, 240(1), 220-234. https://doi.org/10.1016/j.ejor.2014.06.033 google scholar
  • Kaptan, M. (2021a). Risk assessment of ship anchorage handling operations using the fuzzy bow-tie method. Ocean Engineering, 236, 109500. google scholar
  • Kaptan, M. (2021b). Risk assessment for transporting ammonium nitrate-based fertilizers with bulk carriers. Journal of ETA Maritime Science, 9(2), 130-137. google scholar
  • Kaptan, M. (2021b). Risk assessment for transporting ammonium nitrate-based fertilizers with bulk carriers. Journal of ETA Maritime Science, 9(2), 130-137. google scholar
  • Kerner, B. S., & Herrtwich, R. G. (2001). Traffic flow forecasting. At-Automatisierungstechnik, 49(11), 505-511.https://doi.org/10.1524/auto.2001.49.11.505 google scholar
  • Khan, B., Khan, F., & Veitch, B. (2020). A Dynamic Bayesian Network model for ship-ice collision risk in the Arctic waters. Safety Science, 130, 104858. google scholar
  • Khan, S., Khan, F., & Zhang, B. (2012). Reverse e-logistics for SMEs in Pakistan. In Advances in Intelligent and Soft Computing: Vol. 115 AISC (Issue VOL. 2). https://doi.org/10.1007/978-3-642-25349-231 google scholar
  • Khakzad, N., Khan, F., Amyotte, P. (2013). Quantitative risk analysis of offshore drilling operations: A Bayesian approach. Safety science, 57, 108-117. google scholar
  • 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. google scholar
  • Kuzu, A. C., Akyuz, E., Arslan, O. (2019). Application of fuzzy fault tree analysis (FFTA) to maritime industry: a risk analysing of ship mooring operation. Ocean Engineering, 179, 128-134. google scholar
  • Lampis, M., Andrews, J. D. (2009). Bayesian belief networks for system fault diagnostics. Quality and Reliability Engineering International, 25(4), 409-426. google scholar
  • Laskey, K. B. (1995). Sensitivity Analysis for Probability Assessments in Bayesian Networks. IEEE Transactions on Systems, Man, and Cybernetics, 25(6), 901-909. https://doi.org/10.1109/21.384252 google scholar
  • Laskowski, R. (2015). Fault Tree Analysis as a tool for modelling the marine main engine reliability structure. Zeszyty Naukowe Akademii Morskiej w Szczecinie, nr 41 (113(113), 71-77. google scholar
  • Lavasani, S. M. M., Wang, J., Yang, Z., Finlay, J. (2012). Application of MADM in a fuzzy environment for selecting the best barrier for offshore wells. Expert Systems with Applications, 39(3), 2466-2478. https://doi.org/10.1016/j.eswa.2011.08.099 google scholar
  • Lavasani, S. M., Ramzali, N., Sabzalipour, F., Akyuz, E. (2015). Utilisation of Fuzzy Fault Tree Analysis (FFTA) for quantified risk analysis of leakage in abandoned oil and natural-gas wells. Ocean Engineering, 108, 729-737. https://doi.org/10.1016/j.oceaneng.2015.09.008. google scholar
  • Li, Y., Xu, D., Shuai, J. (2020). Real-time risk analysis of road tanker containing flammable liquid based on fuzzy Bayesian network. Process Safety and Environmental Protection, 134, 36-46. google scholar
  • Norrington, L., Quigley, J., Russell, A., Van der Meer, R. (2008). Modelling the reliability of search and rescue operations with Bayesian Belief Networks. Reliability Engineering and System Safety, 93(7), 940-949. https://doi.org/10.1016/j.ress.2007.03.006 google scholar
  • Özaydın, E., Fışkın, R., Uğurlu, Ö., Wang, J. (2022). A hybrid model for marine accident analysis based on Bayesian Network (BN) and Association Rule Mining (ARM). Ocean Engineering, 247, 110705. google scholar
  • Pan, R., Zhou, X., Lin, X. (2012). The assessment of cylinder liner by HAZOP analysis and fuzzy comprehensive evaluation. Advanced Materials Research, 562-564, 650-653. https://doi.org/10.4028/www.scientific.net/AMR.562-564.650 google scholar
  • Ping, P., Wang, K., Kong, D., Chen, G. (2018). Estimating probability of success of escape, evacuation, and rescue (EER) on the offshore platform by integrating Bayesian Network and Fuzzy AHP. Journal of Loss Prevention in the Process Industries, 54(January), 57-68. https://doi.org/10.1016/j.jlp.2018.02.007 google scholar
  • Pristrom, S., Yang, Z., Wang, J., Yan, X. (2016). A novel flexible model for piracy and robbery assessment of merchant ship operations. Reliability Engineering System Safety, 155, 196-211. google scholar
  • Przytula, K. W., Thompson, D. (2000). Construction of Bayesian networks for diagnostics. IEEE Aerospace Conference Proceedings, 5, 193-200. https://doi.org/10.1109/aero.2000.878490 google scholar
  • Raiyan, A., Das, S., Islam, M. R. (2017). Event tree analysis of marine accidents in Bangladesh. Procedia Engineering, 194, 276-283. https://doi.org/10.1016/j.proeng.2017.08.146 google scholar
  • Rajakarunakaran, S., Maniram Kumar, A., Arumuga Prabhu, V. (2015). Applications of fuzzy faulty tree analysis and expert elic-itation for evaluation of risks in LPG refuelling station. Journal of Loss Prevention in the Process Industries, 33, 109-123. https://doi.org/10.1016/j.jlp.2014.11.016. google scholar
  • Sarıalioğlu, S., Uğurlu, Ö., Aydın, M., Vardar, B., Wang, J. (2020). A hybrid model for human-factor analysis of engine-room fires on ships: HFACS-PVFFTA. Ocean Engineering, 217, 107992 google scholar
  • Sayareh, J., Ahouei, V. R. (2013). Failure Mode and Effects Analysis (FMEA) for reducing the delays of cargo handling operations in marine bulk terminals. Journal of Maritime Research, 10(2), 43-50. google scholar
  • 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. https://doi.org/10.1016/j.eswa.2015.02.027 google scholar
  • Senol, Y. E., Sahin, B. (2016). A novel Real-Time Continuous Fuzzy Fault Tree Analysis (RC-FFTA) model for dynamic environment. Ocean Engineering, 127(September), 70-81. https://doi.org/10.1016/j.oceaneng.2016.09.035. google scholar
  • Şakar, C., Zorba, Y. (2017). A study on safety and risk assessment of dangerous cargo operations in oil/chemical tankers. Journal of ETA Maritime Science, 5(4), 396-413. google scholar
  • Şakar, C., Zorba, Y. (2017). A Study on Safety and Risk Assessment of Dangerous Cargo Operations in Oil/Chemical Tankers. Journal of ETA Maritime Science, 5(4), 396-413. google scholar
  • Türkoğlu, N., Menteş A. (2014). Fuzzy Based Risk Analysis for OffShore Petroleum Platforms. Journal of ETA Maritime Science, 2(1), 1-10 google scholar
  • Xin, P., Khan, F., Ahmed, S. (2017). Dynamic hazard identification and scenario mapping using Bayesian network. Process Safety and Environmental Protection, 105, 143-155. google scholar
  • Uğurlu, Ö., Kartal, Ş. E., Gündoğan, O., Aydin, M., Wang, J. (2022). A statistical analysis-based Bayesian Network model for assessment of mobbing acts on ships. Maritime Policy Management, 1-26. google scholar
  • Y ang, Z., Bonsall, S., Wang, J. (2008). Fuzzy rule-based Bayesian reasoning approach for prioritization of failures in FMEA. IEEE Transactions on Reliability, 57(3), 517-528. google scholar
  • Y ang, Z., Yang, Z., Yin, J. (2018). Realising advanced risk-based port state control inspection using data-driven Bayesian networks. Transportation Research Part A: Policy and Practice, 110(August 2017), 38-56. https://doi.org/10.1016/j.tra.2018.01.033 google scholar
  • Y azdi, M., Kabir, S., Walker, M. (2019). Uncertainty handling in fault tree-based risk assessment: State of the art and future perspectives. Process Safety and Environmental Protection, 131, 89-104. google scholar
  • Yuan, Z., Khakzad, N., Khan, F., Amyotte, P. (2016). Domino effect analysis of dust explosions using Bayesian networks. Process Safety and Environmental Protection, 100, 108-116. google scholar
  • Zarei, E., Khakzad, N., Cozzani, V., Reniers, G. (2019). Safety analysis of process systems using Fuzzy Bayesian Network (FBN). Journal of Loss Prevention in the Process Industries, 57(June 2018), 7-16. https://doi.org/10.1016/j.jlp.2018.10.011 google scholar
  • Zarei, E., Yazdi, M., Abbassi, R., Khan, F. (2019). A hybrid model for human factor analysis in process accidents: FBN-HFACS. Journal of Loss Prevention in the Process Industries, 57(August 2018), 142-155. https://doi.org/10.1016/j.jlp.2018.11.015 google scholar
  • Zhang, J., Teixeira, Â. P., Guedes Soares, C., Yan, X., Liu, K. (2016). Maritime Transportation Risk Assessment of Tianjin Port with Bayesian Belief Networks. Risk Analysis, 36(6), 1171-1187. https://doi.org/10.1111/risa.12519 google scholar
  • Zhou, Q., Wong, Y. D., Loh, H. S., Yuen, K. F. (2018). A fuzzy and Bayesian network CREAM model for human reliability analysis - The case of tanker shipping. Safety Science, 105(February), 149-157. https://doi.org/10.1016/j.ssci.2018.02.011. google scholar

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APA

Aydın, M. (2023). Risk Assessment of Solid Bulk Cargo Liquefaction Consequences in Maritime Transportation under a Fuzzy Bayesian Network Approach. Journal of Transportation and Logistics, 8(1), 36-47. https://doi.org/10.26650/JTL.2023.1243766


AMA

Aydın M. Risk Assessment of Solid Bulk Cargo Liquefaction Consequences in Maritime Transportation under a Fuzzy Bayesian Network Approach. Journal of Transportation and Logistics. 2023;8(1):36-47. https://doi.org/10.26650/JTL.2023.1243766


ABNT

Aydın, M. Risk Assessment of Solid Bulk Cargo Liquefaction Consequences in Maritime Transportation under a Fuzzy Bayesian Network Approach. Journal of Transportation and Logistics, [Publisher Location], v. 8, n. 1, p. 36-47, 2023.


Chicago: Author-Date Style

Aydın, Muhammet,. 2023. “Risk Assessment of Solid Bulk Cargo Liquefaction Consequences in Maritime Transportation under a Fuzzy Bayesian Network Approach.” Journal of Transportation and Logistics 8, no. 1: 36-47. https://doi.org/10.26650/JTL.2023.1243766


Chicago: Humanities Style

Aydın, Muhammet,. Risk Assessment of Solid Bulk Cargo Liquefaction Consequences in Maritime Transportation under a Fuzzy Bayesian Network Approach.” Journal of Transportation and Logistics 8, no. 1 (Mar. 2024): 36-47. https://doi.org/10.26650/JTL.2023.1243766


Harvard: Australian Style

Aydın, M 2023, 'Risk Assessment of Solid Bulk Cargo Liquefaction Consequences in Maritime Transportation under a Fuzzy Bayesian Network Approach', Journal of Transportation and Logistics, vol. 8, no. 1, pp. 36-47, viewed 5 Mar. 2024, https://doi.org/10.26650/JTL.2023.1243766


Harvard: Author-Date Style

Aydın, M. (2023) ‘Risk Assessment of Solid Bulk Cargo Liquefaction Consequences in Maritime Transportation under a Fuzzy Bayesian Network Approach’, Journal of Transportation and Logistics, 8(1), pp. 36-47. https://doi.org/10.26650/JTL.2023.1243766 (5 Mar. 2024).


MLA

Aydın, Muhammet,. Risk Assessment of Solid Bulk Cargo Liquefaction Consequences in Maritime Transportation under a Fuzzy Bayesian Network Approach.” Journal of Transportation and Logistics, vol. 8, no. 1, 2023, pp. 36-47. [Database Container], https://doi.org/10.26650/JTL.2023.1243766


Vancouver

Aydın M. Risk Assessment of Solid Bulk Cargo Liquefaction Consequences in Maritime Transportation under a Fuzzy Bayesian Network Approach. Journal of Transportation and Logistics [Internet]. 5 Mar. 2024 [cited 5 Mar. 2024];8(1):36-47. Available from: https://doi.org/10.26650/JTL.2023.1243766 doi: 10.26650/JTL.2023.1243766


ISNAD

Aydın, Muhammet. Risk Assessment of Solid Bulk Cargo Liquefaction Consequences in Maritime Transportation under a Fuzzy Bayesian Network Approach”. Journal of Transportation and Logistics 8/1 (Mar. 2024): 36-47. https://doi.org/10.26650/JTL.2023.1243766



TIMELINE


Submitted28.01.2023
Accepted01.03.2023
Published Online10.08.2023

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