A safety risk assessment for ship boarding parties from fuzzy Bayesian networks perspective

TURNA İ.

MARITIME POLICY & MANAGEMENT, cilt.51, sa.1, ss.1-14, 2024 (SSCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 51 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1080/03088839.2022.2112780
  • Dergi Adı: MARITIME POLICY & MANAGEMENT
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Business Source Elite, Business Source Premier, Communication Abstracts, EconLit, Environment Index, Geobase, Metadex, PAIS International, Pollution Abstracts, Public Affairs Index, DIALNET, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-14
  • Anahtar Kelimeler: Maritime security, maritime piracy, fuzzy Bayesian network (FBN), security, risk assessment, policy, MARITIME SECURITY, ACCIDENTS, MODEL
  • Recep Tayyip Erdoğan Üniversitesi Adresli: Evet

Özet

Many people embark and disembark on merchant ships to perform various duties while ships are moored in ports. Some units, such as marine pilots and coast guards, must board and disembark the ships while they are underway. Boarding and disembarking from ships include some dangers that could result in serious injury or even death. Regulations for pilot boarding arrangements have been developed by organizations such as IMO, ICS, and IMPA to reduce risks. At each Port State Control, Class, and P&I inspection, the condition of the pilot ladders and the accommodation ladders of the ships is inspected. The situation can be much more complicated and risky for boarding parties that have to board ships underway in extraordinary situations such as when pirates or terrorists had full control of the ship. Thus, there is a need for a model, which can identify the importance weightings for each contributing factor that is involved in boarding casualties. This study introduces a technique to identify risk factors for boarding parties through fuzzy Bayesian Networks (FBN). The findings of this research are expected to help boarding parties develop new strategies for their highly risky Opposed Boarding tasks.