ABSTRACT

Previous statistical maritime accident studies are focused on identifying human factors. However, the previous studies were not capable of modelling the complex interrelations that exist between these factors. As accidents are complex processes, researchers fail to agree on the contribution of each human factor. Therefore, in this research study, a new Fuzzy Cognitive Map (FCM)-based technique known as MALFCMs has been introduced and applied. Its novelty is the application of FCM concepts to model the relationships of accident contributors by combining historic accident data with expert opinion. Our approach is capable of integrating information obtained from real occurrences, therefore, the results can be considered more objective. Thus, in this paper, MALFCMs was applied to grounding/stranding accidents in general-cargo vessels, revealing that ‘unprofessional behavior’, ‘lack of training’, and ‘inadequate leadership and supervision’ are the most critical factors, with a normalised importance weighting of 13.25%, 13.24%, and 13.24% respectively.

摘要

以前的统计海上事故研究侧重于识别人为因素。然而，以前的研究无法模拟这些因素之间存在的复杂相互关系。由于事故是复杂的过程，研究人员无法就每个人为因素的贡献达成一致。因此，在本研究中，引入并应用了一种新的基于模糊认知图 (FCM) 的技术，称为 MALFCM。其新颖之处在于应用 FCM 概念，通过将历史事故数据与专家意见相结合，对事故贡献者之间的关系进行建模。我们的方法能够整合从真实事件中获得的信息，因此可以认为结果更客观。因此，在本文中，MALFCM 被应用于普通货船的搁浅/搁浅事故，