Knowledge Graph-Driven Safety Risk Reasoning for Offshore Wind Turbine Installation Vessels with an LR-PDCA Embedding Mechanism

Offshore wind turbine installation vessel (WTIV) accidents have remained frequent, and the long-standing gap between documented safety management system (SMS) files and on-site operations remains unresolved. Existing knowledge graph (KG)-based safety research concentrates heavily on subway and building construction, leaving the WTIV domain largely unexplored. This study constructs a safety risk knowledge graph comprising 266 nodes and 440 edges, using 92 Likelihood–Exposure–Consequence (LEC) risk records and seven accident cases as knowledge sources. Three reasoning modes (multi-hop associative query, LEC coupling inference, and topological analysis) are implemented. The LEC coupling inference relaxes the traditional three-factor independence assumption by using cascading-failure evidence chains from accidents; it provides a formal definition of the coupling coefficient α and five trigger conditions C1–C5, with the resulting coefficients reported as expert-informed and exploratory in status. The KG is embedded into a three-layer LR-PDCA (Leadership–Responsibility–Plan–Do–Check–Act) framework at the document, process, and institutional levels, mapping reasoning outputs to six management processes. Validation at a real-world site shows that the inferred paths match the causal chains of three typical accidents and that the three-layer embedding maps operably onto the case Company’s SMS document structure; quantitative effectiveness metrics and a blinded expert evaluation are specified as a deployment-phase protocol rather than reported as verified results. The generalizability of C-layer rules is constrained by the limited accident sample, and long-term verification using accident rate data remains for future work.