  Automatic Compliance Checking (ACC) within the Architecture, Engineering, and
Construction (AEC) sector necessitates automating the interpretation of
building regulations to achieve its full potential. Converting textual rules
into machine-readable formats is challenging due to the complexities of natural
language and the scarcity of resources for advanced Machine Learning (ML).
Addressing these challenges, we introduce CODE-ACCORD, a dataset of 862
sentences from the building regulations of England and Finland. Only the
self-contained sentences, which express complete rules without needing
additional context, were considered as they are essential for ACC. Each
sentence was manually annotated with entities and relations by a team of 12
annotators to facilitate machine-readable rule generation, followed by careful
curation to ensure accuracy. The final dataset comprises 4,297 entities and
4,329 relations across various categories, serving as a robust ground truth.
CODE-ACCORD supports a range of ML and Natural Language Processing (NLP) tasks,
including text classification, entity recognition, and relation extraction. It
enables applying recent trends, such as deep neural networks and large language
models, to ACC.
