Digital Twin and BIM for Automated Compliance Checking: A Systematic Review of the Missing Link and the Path Toward Generative Compliance
DOI:
https://doi.org/10.66408/abc2.2026.92Keywords:
Automated compliance checking, Building information modelling, Digital twin, Systematic literature review, Knowledge graphs, Generative AI, Large Language ModelsAbstract
Automated Compliance Checking (ACC) is widely proposed as a remedy for inefficient manual building-regulation review, yet it remains unclear whether Building Information Modelling (BIM) or Digital Twin (DT) technology offers the more promising foundation for ACC, and how either relates to the longer-term ambition of generative, compliance-driven design. This paper reports a systematic literature review (SLR) of 141 studies (2018–2026), retrieved through a dual-stream search on Scopus and Web of Science and screened in accordance with PRISMA 2020. The corpus was analysed through three complementary methods: bibliometric trend analysis, keyword co-occurrence network analysis in VOSviewer, and AI-assisted thematic synthesis across four temporal sub-periods. The analysis shows that BIM constitutes the structural and methodological core of the field (134 papers, 22–25% implementation maturity at design and construction stages), whereas DT-related research (7 DT+ACC papers and 9 BIM-DT cross-stream papers) is peripheral, recent, and almost entirely conceptual (0% prototype rate in the cross-stream zone). No direct co-occurrence edge connects “digital twin” and “automated compliance checking” in the keyword network; a structural absence that is independently confirmed by the thematic map as an explicit research-gap entry. This un-operationalized DT–ACC integration is identified as one of the principal research gap in the field. The paper argues that, on present evidence, design-stage BIM is the only substrate simultaneously rich and mature enough to host a generative compliance architecture, while the DT–ACC link remains the field’s longer-horizon target.
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Copyright (c) 2026 Donald Lako, Farzad Pour Rahimian, Ali Saad

This work is licensed under a Creative Commons Attribution 4.0 International License.