Towards semantic enrichment of IFC models through language modeling

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Fuchs, Stefan and Borrmann, André; Moreno-Rangel, Alejandro and Kumar, Bimal, eds. (2025) Towards semantic enrichment of IFC models through language modeling. In: EG-ICE 2025. University of Strathclyde Publishing, GBR, pp. 506-515. ISBN 9781914241826 (https://doi.org/10.17868/strath.00093271)

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Abstract

Semantic enrichment of Building Information Models (BIM) is critical for improving model quality and supporting downstream applications, such as compliance checking and design automation. This study explores continued pre-training of large language models on building information models in the Industry Foundation Classes (IFC) format to infer missing information required for Automated Compliance Checking (ACC). By evaluating encoder-only and decoder-only transformer architectures across different test cases, we demonstrate how much information can be inferred automatically and identify which model types and data representations are most suitable for the task. A modified ifcJSON-5a* representation was found to improve performance over traditional EXPRESS formats. The findings suggest that domain-adapted language models are a promising approach for advancing semantic enrichment in BIM and open new directions for integrating AI more deeply into the building design process.

  • Item type:Book Section
    ID code:93271
    Dates:
    Date
    Event
    1 July 2025
    Published
    9 June 2025
    Accepted
    Subjects:Fine Arts > Architecture
    Department:Faculty of Engineering > Architecture
    Depositing user: Pure Administrator
    Date deposited:27 Jun 2025 10:53
    Last modified:29 Jun 2026 09:40
    URI:https://strathprints.strath.ac.uk/id/eprint/93271
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