Architectural Technology and AI-Enabled BIM Compliance: A Global Framework for Cognitive, Self-Governing Regulatory Ecosystems

Abstract

transformation of the architecture and construction industry is the transition from a manual drafting process to an extremely complex environment of building models that contain lots of building data – Building Information Modeling (BIM). But keeping these complex and detailed digital models accurate and sound to meet strict regulation requirements is still extremely manual, with an increased risk of project delays, human error, and expensive delays. This paper proposes to create an AI-powered BIM Compliance Ecosystem that can link these two areas together. The proposed architecture combines the different functionality of cognitive computing, natural language processing (NLP), the knowledge graph and the open data standards (such as IFC) to convert the building codes, which are highly unstructured, to machine-readable semantic logic. It is a comparative study and places in parallel varying levels of maturity and artificial intelligence and of regulatory complexity – deterministic codes, prescriptive codes, open-ended performance based standards. Furthermore, it addresses critical problems like data interoperability, professional liability, and the need for Explainable AI (XAI) in order to make AI transparent in design. The results of this study ultimately offer a roadmap - based on empirical data - to inform and support future prototyping and simulation testing, as well as the rollout of self-governing architectural compliance frameworks across the industry.