A Text-Driven, Human-Centric Framework for Sustainable Safety Risk Governance in Steel Manufacturing: Integrating NLP, Topic Modeling, and Bayesian Decision Fusion
TL;DR
It is demonstrated that unstructured safety text can be transformed into actionable risk intelligence, enhancing the proactive, systematic, and adaptive governance of safety risks.
Abstract
The transition toward Industry 5.0 requires safety management systems that are not only intelligent and data-driven but also human-centric, resilient, and aligned with sustainable operations. However, conventional safety risk assessment in steel manufacturing remains heavily dependent on expert judgment and often lacks the adaptability required to address complex and dynamic production environments. This study proposes a text-driven intelligent framework for sustainable safety risk governance by integrating natural language processing, topic modeling, objective indicator weighting, and Bayesian decision fusion. The framework establishes a closed-loop process encompassing risk identification, quantitative assessment, risk classification, and hierarchical control. It automatically extracts risk-related information from unstructured safety records, maps the identified hazards onto a human–machine–environment–management structure, quantifies multidimensional risk indicators, and translates assessment outcomes into differentiated control measures. The framework was evaluated using field safety records collected from Tianjin Iron and Steel Group. The topic modeling results identified four major dimensions of operational risk, while the CRITIC–Bayesian weighting mechanism combined data-driven indicator differentiation with context-sensitive probabilistic reasoning. Following its integration into the company’s intelligent safety management platform and one year of operational use, the framework reduced the time required to formulate safety inspection plans by 70%, supported dynamic four-level risk classification, and achieved a 97% task completion rate. The number of recorded safety accidents also decreased by 35% compared with the pre-deployment baseline. These findings demonstrate that unstructured safety text can be transformed into actionable risk intelligence, enhancing the proactive, systematic, and adaptive governance of safety risks. The proposed framework provides a practical pathway for advancing human-centric safety management, operational resilience, and sustainable production in the steel industry.