Deep Learning-Based Safety Legislation Recommendation System for Construction Safety Reports

Ensuring legally compliant safety and health documentation remains a significant challenge in construction projects because practitioners often lack expertise in identifying and applying relevant statutory provisions. This study proposes a deep learning-based legislation recommendation system to reduce inconsistencies in statutory citation and improve the legal traceability of safety documentation. The system integrates domain-specific ontologies and context-aware language models to recommend appropriate legal provisions based on user-inputted risk factors and keywords. For empirical validation, the system was applied to the Design for Safety (DfS) report, a representative safety document prepared during the design phase of construction projects. A training dataset comprising 1355 DfS reports and 356 safety legislation articles was used, with semantic relationships enhanced through ontology-based vocabulary expansion and Word2Vec embeddings. KoELECTRA, a Korean pre-trained language model, achieved the best performance, with top-1 accuracy of 58.1%, F1-score of 56.6%, and top-3 accuracy of 71.8%. A web-based application was also developed to support legal referencing during document preparation. The findings demonstrate the system’s potential to assist practitioners in identifying relevant legislation, enhance regulatory compliance, and improve the consistency and quality of construction safety documentation.