From incident narratives to actionable controls: insights on the iron & steel industry using LLM assisted learning from incident databases

Abstract

Learning from incidents is a cornerstone of occupational safety risk management, especially in high-risk industrial sectors. Incident databases support this process by collecting records that describe the causes, dynamics, and consequences of adverse events. However, these databases largely rely on unstructured textual narratives, which limits systematic analysis and the translation of learned lessons into effective preventive actions. This paper focuses on the iron and steel industry and presents an analysis pipeline supported by Large Language Models (LLMs) for extracting, synthesising, and structuring information from two major incident databases: the U.S. OSHA database and the French ARIA database. Relevant records were selected using industry classification codes and pre-processed to harmonise terminology, normalise information fields, remove duplicates, and manage multilingual content. Within a human-in-the-loop framework, LLMs were used to identify critical occupational risk scenarios, characterise them in terms of frequency and severity, and derive prevention and risk mitigation measures structured according to the ISO 45001 hierarchy of controls. Eight critical scenarios were identified and subsequently validated and refined by safety experts from the steel industry. Quantitative analysis identified point-of-operation machinery and load handling as the most frequent scenarios, while confined spaces and high-energy events exhibited disproportionate severity and lethality. The results demonstrated how LLM-supported approaches can enhance learning from incidents by transforming large volumes of heterogeneous narrative data into a traceable, expert-validated knowledge base that supports hazard identification, risk assessment and management, and continuous improvement in high-risk industrial environments.