Submodel Comparison Method Based on IFC File Content
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 4
Abstract
Because large-scale building projects are designed through multiprofessional collaboration and the design changes are unavoidable and frequent, it is essential to efficiently detect the changes between submodels in different versions for building projects. However, the traditional methods may have lower efficiency problems, such as time-consuming and inaccurate results via manual operation and visual inspection. To address these issues, this paper proposes a submodel comparison method based on Industry Foundation Class (IFC) file contents, called as IFC-submodel content comparison (SCC). First, all instances in two IFC files were, respectively, preprocessed and stored in a unified format. Next, all instances related with the required demand were extracted to form submodels. Then, the instances of the two submodels were all checked to remove redundancies, respectively, and each remaining instance was assigned at least one signature, which is a string consisting of feature information. Finally, these instances in the two submodels were compared by their signatures in a breadth-first search manner, and the matched information and unmatched information were extracted. The experimental results showed that the proposed IFC-SCC method is superior compared with the previous methods. Compared with the globally unique identifier (GUID)-based comparison method, the IFC-SCC method generated more accurate matched information, and obtained an improvement in time efficiency by 16.85%. Compared with the flat-based comparison method, the IFC-SCC method had an improvement of 48.35% on the time efficiency on average and removed the redundancies.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies. https://gr.xjtu.edu.cn/web/xzdu/bim_ifc/submodel.
Acknowledgments
The work described in this paper is partially supported by the Chinese National Natural Science Foundation (Grant No. 11975182) and the State Key Laboratory of Rail Transit Engineering Information in China (Grant No. 2017-05).
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Published In
Journal of Computing in Civil Engineering
Volume 36 • Issue 4 • July 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jul 1, 2021
Accepted: Feb 23, 2022
Published online: Apr 27, 2022
Published in print: Jul 1, 2022
Discussion open until: Sep 27, 2022
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