An Integrated Framework for Automated Identification of Workers’ Safety Violation Based on Knowledge Graph

Automatic identification of worker safety violations can substantially strengthen construction-site safety management by enabling continuous, real-time monitoring. Although recent advances have made automated detection feasible, many existing systems still suffer from poor adaptability and limited extensibility. To address these limitations, this study proposes an integrated, knowledge graph-based framework for automatic identification of workers’ safety violations. The framework comprises two principal components: (1) a knowledge graph construction module that encodes domain knowledge (safety regulations, task–hazard relationships, and contextual constraints) into a machine-readable graph structure and (2) a graph-enabled violation identification module that maps structured scene descriptions of worker and environmental states to the knowledge graph and performs semantic inference to detect violations. In this study, these structured scene descriptions are manually specified and simulated as subject–predicate–object triplets; integration with raw sensing data is left for future work. For validation, we construct a knowledge graph containing 1200 safety rules and evaluate the violation identification module on 500 annotated examples representing realistic worker scenarios. Using this curated knowledge graph and structured inputs, the proposed approach achieves an identification accuracy of 97.6% for unsafe worker behaviors. Experimental analysis shows that the knowledge graph representation substantially improves the system’s expandability and interpretability compared with traditional hard-coded rules, facilitating easier incorporation of new rules and multimodal sensing inputs. The results indicate that knowledge graph-driven reasoning offers a practical, scalable pathway for robust, context-aware safety violation detection in varied construction environments.