Computational Geometric Approach for BIM Semantic Enrichment to Support Automated Underground Garage Compliance Checking
Publication: Journal of Construction Engineering and Management
Volume 148, Issue 1
Abstract
The need for manual semantic annotation to domain-specific building information modeling (BIM) models limits the application of BIM technology in various architecture, engineering, and construction fields. BIM semantic enrichment refers to a procedure that can identify missing information and improve interoperability across different applications. The need for semantic enrichment of BIM models is often emphasized in the literature; however, few studies have shown the potential of semantic enrichment approaches in dealing with problems in real-world cases. To provide an empirical demonstration in the practice, an example of BIM semantic enrichment for supporting underground garage compliance checking is selected as a case study. Geometric reasoning is one of the effective approaches to enhance semantic information in BIM models to support real-world applications. In this study, a computational geometric approach is developed to demonstrate how geometric information can be extracted and used to infer missing semantics in underground garage BIM models. A prototype system is developed, and real underground garage BIM models are used to validate the effectiveness of the proposed approach. The contribution of this study is that it provides the detailed implementation of performing geometric reasoning to enhance BIM semantics in real-world cases. Additionally, the findings of this case study show that the proposed computational geometric approach has the potential to enhance the semantics of BIM models to support decision making in real-world engineering problems, and the developed algorithms can be used to automate the compliance checking process of the underground garage.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research is partly supported by Major Science & Technology Project of Hubei (2020ACA006), National Natural Science Foundation of China (Grant Nos. 71732001, 51878311, 51978302), and China Scholarship Council (No. 202006160115). Thanks to Professor Timo Hartmann (Technical Univ. of Berlin) for his advice on this paper.
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Published In
Journal of Construction Engineering and Management
Volume 148 • Issue 1 • January 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Apr 25, 2021
Accepted: Oct 11, 2021
Published online: Nov 9, 2021
Published in print: Jan 1, 2022
Discussion open until: Apr 9, 2022
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