Bridging Building Information Modelling and Natural Language Processing: A Systematic Review on Current Applications and Limitations

Proceedings of the ... ISARC

Abstract

Building Information Modelling (BIM) is widely recognised as a key driver of digital transformation in the architecture, engineering and construction industry.However, its full potential remains underutilised, partly due to the vast and complex nature of BIM-related data.At the same time, Natural Language Processing (NLP), a rapidly evolving field of artificial intelligence, offers advanced methods for interpreting and leveraging text-based information.The convergence of these two technologies holds considerable potential to facilitate human interaction with BIM data by making it more accessible.Despite this potential, research directly linking NLP and BIM remains limited.This paper attempts to bridge this gap by mapping and evaluating the current state of research on NLP applications within BIM workflows.To this end, a Systematic Literature Review is conducted in accordance with the methodological framework proposed by Fink, identifying 92 relevant publications.The metadata of these publications was analysed using VOSviewer, followed by a detailed content analysis.For each source, the BIM-related tasks optimised by NLP and NLP functionality applied to BIM data are identified, and a novel approach is proposed to classify the BIM-NLP integration into five categories: Extract, Query, Enrich, Modify, and Match.The analysis reveals that most existing literature explores NLP application in a peripheral or exploratory manner, with few instances of deep integration into BIM workflows.Nevertheless, the reviewed literature highlights the potential of NLP to improve data accessibility, automate information processing, and enhance human-machine interaction.

Authors 4

1. Tessa Marie Oberhoff Aachen Institute for Construction Management

   RWTH Aachen University

   Affiliation as printed

   Institute for Construction Management , Digital Engineering and Robotics in Construction RWTH Aachen University , Germany

2. Julian Cloos Aachen Institute for Construction Management

   RWTH Aachen University

   Affiliation as printed

   Institute for Construction Management , Digital Engineering and Robotics in Construction RWTH Aachen University , Germany

3. Sven Mackenbach Aachen Institute for Construction Management

   RWTH Aachen University

   Affiliation as printed

   Institute for Construction Management , Digital Engineering and Robotics in Construction RWTH Aachen University , Germany

4. Katharina Klemt‐Albert Aachen Institute for Construction Management

   RWTH Aachen University

   Affiliation as printed

   Institute for Construction Management , Digital Engineering and Robotics in Construction RWTH Aachen University , Germany