Automating building code interpretation using NLP and transfer learning for enhanced code compliance

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Ali, Sherief and König, Markus; Moreno-Rangel, Alejandro and Kumar, Bimal, eds. (2025) Automating building code interpretation using NLP and transfer learning for enhanced code compliance. In: EG-ICE 2025. University of Strathclyde Publishing, GBR, pp. 108-113. ISBN 9781914241826 (https://doi.org/10.17868/strath.00093234)

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Abstract

Building codes are essential for ensuring safety, quality, and regulatory compliance in construction projects. Currently, extracting information from regulatory texts is manual, time-consuming, and prone to errors, particularly because it involves interpreting natural language. This paper focuses on leveraging deep learning to train a model capable of predicting essential entities from regulatory texts, thereby enabling the interpretation and understanding of the natural language used in regulations. We fine-tuned a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model to perform entity recognition for CODE-ACCORD dataset, which contains English and Finnish regulatory clauses annotated with BIO (Begin, Inside, Outside) tags across four essential categories: Object, Property, Quality, and Value. The trained model achieved high precision in entity recognition, effectively generalizing predictions to previously unseen regulatory clauses.. This paper contributes primarily by automating the semantic interpretation of regulatory texts, thereby establishing a foundation for more efficient and accurate automated code compliance checking processes.

  • Item type:Book Section
    ID code:93234
    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:26 Jun 2025 15:23
    Last modified:20 May 2026 00:37
    URI:https://strathprints.strath.ac.uk/id/eprint/93234
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