Abstract
Ship collisions emerge from interactions among human, vessel, environmental, and management factors. Aggregate accident networks can, however, combine relations across events and evidence levels, obscuring whether recurrent topology represents event-level pathways. We coded 364 official Chinese collision investigation reports using Systems-Theoretic Process Analysis and constructed a directed binary network of 46 factors and 269 relations. Induced triads were evaluated against degree-preserving and functional block-preserving null models and then traced to accidents, responsible vessels, and responsibility chains. Four triads, 021C, 021U, 021D, and 030T, were overrepresented in the primary network. Frequency weighting preserved H1, H9, and H19 as the three highest-ranked nodes by total strength, whereas only 030T remained concentrated on high-support edges after block-stratified weight permutation. Evidence-tier filtering was more restrictive: the cumulative E1–E4 network retained 112 relations, and none of the four motifs remained significant under the functional block-preserving null model. Event-level support was strongest for 021C and 021U, whereas 030T mainly reflected aggregate transitive closure. The primary motifs therefore characterize structures generated by the complete report coding framework, not experimentally identified causal mechanisms. Combining constrained topology, recurrence weights, evidence grading, and event-level back-tracing provides an auditable basis for safety questions without conflating structural enrichment with causal strength.