Hierarchical Representation and Deep Learning–Based Method for Automatically Transforming Textual Building Codes into Semantic Computable Requirements
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 5
Abstract
Most of the existing automated compliance checking (ACC) systems are unable to fully automatically convert building-code requirements, especially requirements that have hierarchically complex semantic and syntactic structures, into computer-processable forms. The state-of-the-art rule-based ACC methods that are able to deal with complex requirements are based on information extraction and transformation rules, which are inflexible when applied to different types of regulatory documents. More research is thus needed to develop a flexible method to automatically process and understand requirements to support the downstream tasks in ACC systems, such as information matching and compliance reasoning. To address this need, this paper proposes (1) a new representation of requirements, the requirement hierarchy, and (2) a deep learning-based method to automatically extract semantic relations between words from building-code sentences, which are used to transform the sentences into such hierarchies. The proposed method was evaluated using a corpus of sentences from multiple regulatory documents. It achieved high semantic relation and requirement hierarchy extraction performance.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request (trained semantic relation extraction model and labeled gold standard data).
Acknowledgments
The authors would like to thank the National Science Foundation (NSF). This material is based on work supported by the NSF under Grant No. 1827733. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
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Published In
Journal of Computing in Civil Engineering
Volume 36 • Issue 5 • September 2022
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© 2022 American Society of Civil Engineers.
History
Received: Jun 24, 2021
Accepted: Nov 23, 2021
Published online: Jun 30, 2022
Published in print: Sep 1, 2022
Discussion open until: Nov 30, 2022
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