AI Framework Improves Maritime Accident Root Cause Analysis Using Historical Tribunal Reports
Researchers have proposed a multi-field hybrid retrieval-augmented generation (RAG) system that automates root cause analysis for maritime accidents using 13,329 Korean tribunal reports spanning 1971 to 2025. The system structures historical adjudication records into searchable 'incident cards' and combines sparse and dense retrieval methods to surface relevant precedents. The work could significantly reduce the manual burden on maritime safety investigators by enabling faster, more consistent evidence-based reporting.
A research team has developed a retrieval-augmented generation (RAG) framework designed to automate maritime accident root cause analysis (RCA) by drawing on a comprehensive dataset of 13,329 Korea Maritime Safety Tribunal (KMST) reports from 1971 to 2025. The system converts raw adjudication documents into structured 'incident cards' indexed across three fields—Summary, Causes, and Disposition—and organizes causes within a hierarchical L1/L2 taxonomy. Retrieval is performed using a field-aware hybrid strategy that fuses sparse and dense rankings through Reciprocal Rank Fusion (RRF), substantially improving NormRecall@100 from a baseline of 0.18 to 0.55. When retrieved precedents are used to ground a large language model's generation, the LLM-as-a-judge quality score rises from 3.34 to 3.72 compared to an LLM-only baseline. Because large-scale expert relevance labels were unavailable, the team evaluated retrieval using ceiling-normalized recall and nDCG derived from metadata-based proxy relevance scores. The authors argue the framework can streamline maritime safety workflows by enabling investigators to quickly locate relevant precedents and draft more consistent, evidence-based reports.
What's missing
The study relies on a proxy relevance score derived from metadata rather than expert-labeled ground truth, which may not fully capture true retrieval quality. The generalization of the framework beyond Korean Maritime Safety Tribunal reports to other jurisdictions or languages is not evaluated. The LLM-as-a-judge scoring methodology introduces its own potential biases and is not validated against human expert assessments of RCA quality.
What different sources said
- arXiv cs.AICenter
Multi-Field Hybrid Retrieval-Augmented Generation for Maritime Accident Root Cause Analysis
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