TY - JOUR
T1 - Hierarchical Representation and Deep Learning-Based Method for Automatically Transforming Textual Building Codes into Semantic Computable Requirements
AU - Zhang, Ruichuan
AU - El-Gohary, Nora
N1 - Funding Information:
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.
Publisher Copyright:
© 2022 American Society of Civil Engineers.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - 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.
AB - 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.
KW - Code checking
KW - Deep learning
KW - Requirement representation
KW - Semantic relation extraction
KW - Semi-supervised learning
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U2 - 10.1061/(ASCE)CP.1943-5487.0001014
DO - 10.1061/(ASCE)CP.1943-5487.0001014
M3 - Article
AN - SCOPUS:85133696213
SN - 0887-3801
VL - 36
JO - Journal of Computing in Civil Engineering
JF - Journal of Computing in Civil Engineering
IS - 5
M1 - 04022022
ER -