A resilience assessment framework for shipping companies which learns from past accidents by using a Fuzzy Cognitive Maps-based approach

Student thesis: Doctoral Thesis

Abstract

The maritime sector has strived to reduce accidents and their consequences since its beginning, by addressing safety as the priority from the design stage to decommissioning of any vessel. Previous accident studies are focused on identifying Human Factors (HFs) in past maritime accidents. However, these studies have failed to identify deeper relations amongst the aforementioned HFs. Then, there has been a lack of detailed technique, which is capable of modelling the complex interrelations between these factors.In addition, the maritime sector has traditionally presented a reactive approach to accidents, as regulations are generally developed to prevent reoccurrence rather than to avoid accident scenarios. However, a higher percentage of the time the system is safe, so it is possible to obtain additional useful information when focusing on the positive events and by learning from them.The aim of this research study is to develop a theoretical understanding and a practical framework to describe how HFs in maritime accidents can more cleverly be identified and linked to the resilience engineering abilities, which will allow assessing the resilience level within a shipping company. Thus, by achieving the aforementioned aim, it is expected to improve overall safety within the maritime domain.Therefore, in this research study, a new technique for Marine Accident Learning with Fuzzy Cognitive Maps (MALFCMs) is introduced and explained. The novelty of MALFCMs is the application of fuzzy cognitive maps (FCMs) to model the relationships of accident contributors by utilizing information directly from an accident database with the ability to combine expert opinion. Therefore, a key aspect to consider in this approach is the data selection, as a qualitative database will increase the success of the aforementioned MALFCMs technique.In addition, as each fuzzy cognitive map will be derived from real occurrences, the results can be considered more objective, and MALFCMs may overcome the main disadvantage of fuzzy cognitive maps by eliminating or controlling the subjectivity in results.Moreover, in this research study, the resilience assessment framework that was developed in EU funded FP7 SEAHORSE project is modified to incorporate additional resilience abilities, and applied to a shipping company. The modified resilience assessment framework, which consists of six phases, is proposed with the aim to assess the resilience level in a shipping company, based on how the company performs on certain resilience abilities, which are linked to common human causes of accidents.Thus, the resilience assessment framework allows first to measure the resilience level in a shipping company by providing a resilience score, which can be benchmarked with other shipping companies. Second, it allows identifying areas for improvement to increase the company resilience level. Finally, it provides a set of recommendations for resilience improvement that may serve the company for future research.
Date of Award28 Jul 2020
Original languageEnglish
Awarding Institution
  • University Of Strathclyde
SponsorsUniversity of Strathclyde
SupervisorRafet Kurt (Supervisor) & Osman Turan (Supervisor)

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