From text to network: a framework for identifying causal factors and risk propagation paths in maritime accidents

Abstract

To systematically investigate the complex causal mechanisms of maritime accidents, this study proposes an automated analytical framework that integrates Natural Language Processing (NLP) with complex network theory. The framework is designed to transform unstructured accident investigation reports into a quantifiable causal network that reflects systemic risk. Drawing on 564 official reports, this study constructs a standardised dataset of causal factors through a two-stage process combining automated preprocessing and manual coding. NLP techniques are then employed to extract causal relationships from the texts, enabling the construction of a weighted, directed complex network from discrete factors. To ensure the reliability of the framework, the extracted causal logic is verified by a domain expert panel, and the identified risk propagation patterns are validated against representative empirical cases. Topological analysis reveals that the causal network exhibits the “small-world” and “scale-free” properties characteristic of complex systems, indicating a high potential for efficient risk propagation mediated by a few key hubs. A multi-dimensional centrality assessment identifies static risk sources of high influence, including “Inadequate Supervision”, “Vessel Stability/Stowage Issues”, and “Adverse Weather/Sea State”. Furthermore, a risk pathway identification algorithm is applied to extract five typical risk propagation patterns. These pathways dynamically illustrate the systemic process by which risk evolves from latent managerial failures, through technical vulnerabilities and the actions of front-line personnel, to a major accident when triggered by specific environmental conditions. This work provides a dynamic, systematic network perspective for accident causation analysis, and its findings offer more precise intervention targets and process-based preventive strategies for maritime safety management.

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