Operational risk identification of maritime surface autonomous ship: A network analysis approach

Maritime autonomous surface ships (MASS) have gained increasing attention from both academia and industry. The safety of MASS is a critical concern of maritime stakeholders. Accordingly, the identification and understanding of related risks have become important for the improvement of their autonomy levels and safe operations. With this perspective, this research aims to identify potential operational risks of MASS and examine their intertwined causal relationships using network modeling. A directed network is established based on the identity that shoulders a specific operational function and causal relationships drawing from academic and gray literature. The single-risk and multiple-risk identification are realized via network modeling. Moreover, network metrics, including the density, betweenness centrality, and reachability, are measured, and the community structure among potential risks is examined. This research contributes to the existing literature by providing an integrative approach to operational risk analysis and an improved understanding of the potential risks in MASS operations. The results shed light on the architecture of potential operational risks, providing managerial implications for MASS risk control and safe operations.