arXiv:2606.13249v1 Announce Type: new Abstract: Maritime accident adjudication reports contain critical tribunal findings for root cause analysis (RCA), yet retrieving relevant precedents and drafting consistent reports from decades of records remains labor-intensive. This paper proposes a multi-field hybrid retrieval-augmented generation (RAG) framework for automated maritime RCA, utilizing a comprehensive dataset of 13,329 Korea Maritime Safety Tribunal (KMST) reports (1971-2025). We transform raw adjudications into a structured knowledge base of "incident cards", indexing three distinct fie
The increasing availability of large, domain-specific datasets combined with advancements in RAG frameworks allows for practical applications of AI in knowledge-intensive fields like maritime accident analysis.
This development indicates the growing capability of AI to automate complex, labor-intensive analytical tasks in highly regulated and critical sectors, setting a precedent for similar applications.
The process of maritime accident root cause analysis can become significantly more efficient and consistent, reducing manual effort and potentially improving safety outcomes through faster, data-driven insights.
- · Maritime Safety Tribunals
- · Insurance companies
- · Shipping industry
- · AI/ML solution providers
- · Human incident researchers (routine tasks)
- · Consulting firms specializing in manual RCA
Automated systems begin to assist, and later lead, in the analysis of maritime accident data.
Improved consistency and speed in identifying accident root causes lead to more effective preventative measures and regulatory adjustments.
The methodology is replicated across other transport and industrial accident analysis domains, creating a new standard for safety investigation and knowledge management.
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Read at arXiv cs.AI