A knowledge-driven approach for automated fire safety compliance checking in operational buildings

Building fire safety compliance remains a critical but labor-intensive task, particularly during the operational phase when new hazards can arise from post-occupancy modifications, equipment degradation, or improper use of space. Existing automated compliance checking (ACC) methods have primarily focused on the design phase and rely on static BIM data, offering limited adaptability to dynamic, in-use conditions. This study presents a formalized, knowledge-based approach for automating fire safety compliance monitoring during building operation. The proposed approach integrates heterogeneous data, including building layouts, in-situ images, and regulatory clauses, within a unified reasoning architecture combining multimodal perception, semantic integration, and rule-based inference. Comprehensive experiments across three compliance coverage categories validated the perception-reasoning pipeline, achieving high accuracy in layout extraction (pixel accuracy = 0.90) and safety asset detection (mAP = 0.91). The domain-adapted Fire Compliance VQA further achieved notable improvements in compliance description accuracy compared with a generic vision-language baseline across BLEU and ROUGE metrics. The results confirm the feasibility of translating observational evidence into clause-grounded compliance decisions. This study extends ACC into the operational phase and establishes a foundation for automated, evidence-based compliance monitoring across dynamic building environments.