Abstract
An emergency operating procedure (EOP) for flooding incidents is used to assist crews in preventing ships from capsizing. However, under a flooding scenario, failure to complete the EOP within a limited time may result in the risk of capsizing. Human performance is the major factor in the EOP execution process, which is influenced by available time. There is a mutual dependence between human error and available time: (a) shorter available time will increase time pressure and the human error probability (HEP); (b) human error will either be recovered, which may require more response time and result in shortened available time, or be uncorrected, which may worsen the system state and reduce the time limitation, thereby shortening the available time. This mutual dependence can affect EOP risk, which is not considered in current studies. This paper proposes a method based on a Dynamic Bayesian Network (DBN) to assess EOP risk considering this mutual dependence. To model the mutual dependence, a continuous SPAR-H method is proposed in the intra-slice network to determine the conditional probability distribution of human error for dependence (a), and a dynamic available time model is proposed in the inter-slice network to determine the conditional probability distribution of available time for dependence (b). The Ro-Ro flooding incident is used to illustrate the proposed method.