  Compliance checking is an essential part of a construction project. The
recent rapid uptake of building information models (BIM) in the construction
industry has created more opportunities for automated compliance checking
(ACC). BIM enables sharing of digital building design data that can be used for
compliance checking with legal requirements, which are conventionally conveyed
in natural language and not intended for machine processing. Creating a
computable representation of legal requirements suitable for ACC is complex,
costly, and time-consuming. Large language models (LLMs) such as the generative
pre-trained transformers (GPT), GPT-3.5 and GPT-4, powering OpenAI's ChatGPT,
can generate logically coherent text and source code responding to user
prompts. This capability could be used to automate the conversion of building
regulations into a semantic and computable representation. This paper evaluates
the performance of LLMs in translating building regulations into LegalRuleML in
a few-shot learning setup. By providing GPT-3.5 with only a few example
translations, it can learn the basic structure of the format. Using a system
prompt, we further specify the LegalRuleML representation and explore the
existence of expert domain knowledge in the model. Such domain knowledge might
be ingrained in GPT-3.5 through the broad pre-training but needs to be brought
forth by careful contextualisation. Finally, we investigate whether strategies
such as chain-of-thought reasoning and self-consistency could apply to this use
case. As LLMs become more sophisticated, the increased common sense, logical
coherence, and means to domain adaptation can significantly support ACC,
leading to more efficient and effective checking processes.
