Using FRAM for Causal Analysis of Marine Risks in the Motor Vessel Milano Bridge Accident: Identifying Potential Solutions

Abstract

The levels of informatization, automation, and intelligence are continuously improving; however, the risks associated with the increased design and operational complexity of ship systems are increasing. Large-scale ship accidents can occur for several reasons. Existing accident analysis methods that examine marine accidents from the perspective of causal one-to-one correspondence have limitations in systematically analyzing complex marine risks during cause identification for the prevention of similar accidents. This study focuses on a systematic causality analysis of the factors related to human error in marine accidents that may occur during the arrival and departure of mega container ships. In particular, a representative case of the Motor Vessel (MV) Milano Bridge crane contact accident at Busan New Port is considered. To explore the complex organizational–technical, human–technical, and organizational–human relationships relevant to this case, human factors (seafarer, pilot, etc.) that are closely related to the linked causes were analyzed using the functional resonance analysis method. This study aims to reduce human error and prevent marine accidents, including pilotage.

A Bibliometric Analysis and Overall Review of the New Technology and Development of Unmanned Surface Vessels

“…To speed up autonomy, the Unmanned Maritime Systems Program Office (PMS 406) is enhancing autonomy in unmanned maritime vehicles through the Unmanned Maritime Autonomy Architecture (UMAA) for software standards and the Rapid Autonomy Integration Lab (RAIL) for developing new capabilities [75]. Intelligence and autonomy likewise show their advantage in risk analysis [76].…”