Buildings, Volume 14, Issue 7 (July 2024) – 372 articles
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This study introduces an innovative framework for automated compliance checking in Building Information Modeling (BIM). By integrating large language models (LLMs), deep learning, and ontology knowledge models, the framework significantly enhances the accuracy and efficiency of compliance checks. The approach automates the processing of regulatory texts, reduces the need for manual intervention, and offers a scalable solution for architects, engineers, and regulators. This novel combination provides a robust understanding of language and faciliates the structured extraction of information, enhancing the compliance verification processes of the construction industry. View this paper
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