Automated compliance checking in AEC in the era of AI and LLMs: A review (2022–2025)

Senousy, Youssef, Cheung, Franco, Beach, Thomas ORCID: https://orcid.org/0000-0001-5610-8027, Elezaj, Ogerta and Vakaj, Edlira 2026. Automated compliance checking in AEC in the era of AI and LLMs: A review (2022–2025). Automation in Construction 191 , 107165. 10.1016/j.autcon.2026.107165

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Abstract

Since 2022, advances in Artificial Intelligence (AI) and Large Language Models (LLMs) have reshaped Automated Compliance Checking (ACC) in AEC. This review applies an AI-driven PRISMA workflow combining LLM-assisted discovery and screening with transparent provenance and audit trails. Studies are mapped to five ACC pipeline stages: rule interpretation, model preparation, rule execution, reporting, and decision support. Iterative coding identifies ten cross-cutting themes used as analytical lenses: Multimodal Information Extraction, Formalisation of Regulatory Text, Semantic Alignment with BIM/IFC, Integration of Ontologies and Knowledge Graphs, Rule Representation and Reasoning, Model-Driven Compliance Intelligence, Tool Development and Real-World Application, Explainability and Trust in AI Systems, Human-in-the-Loop Approaches, Evaluation and Benchmarking. The analysis examines their presence across stages, highlights the rise of LLM-assisted rule discovery, and identifies assurance practices. The review presents a stage-based gap analysis, an evidence-based agenda for interpretable, auditable, multimodal ACC, and a reproducible method for maintaining a living review over time.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Elsevier
ISSN:	0926-5805
Date of First Compliant Deposit:	10 August 2026
Date of Acceptance:	21 July 2026
Last Modified:	10 Aug 2026 10:45
URI:	https://orca.cardiff.ac.uk/id/eprint/188810

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