AI for Regulatory and Code Compliance

Manual code compliance is an inefficient process vulnerable to human error. This paper addresses the challenge of systemic automation by introducing a two-stage NLP framework that processes entire building codes rather than fragmented sections. Through semantic hierarchy mapping and ontological rule generation, the proposed system translates natural language into verifiable logic for BIM integration. Comparative analysis indicates that this framework outperforms existing rule-based systems in handling complex cross-references and regulatory exceptions. This research provides architects and engineers with a transparent, trustworthy mechanism for automated decision-making, establishing a scalable foundation for the next generation of digital building regulations.