Research on a BIM Model Quality Compliance Checking Method Based on a Knowledge Graph
Publication: Journal of Computing in Civil Engineering
Volume 39, Issue 1
Abstract
With the development of information technology, building information modeling (BIM) is becoming widely used in the construction industry. It is very important to ensure the quality and compliance of BIM models. To solve the problem of time-consuming and laborious traditional checking methods and the limitation that existing software hard coding cannot adapt to rule changes, this paper proposes a BIM model quality compliance checking method based on knowledge graph that can quickly check BIM models through automated checking and ensure the modeling quality of BIM models. Firstly, knowledge extraction for BIM modeling standards and delivery standards is carried out based on knowledge graph technology to structurally represent text. Then, Revit secondary development technology and the C# programming language are adopted to develop a BIM model quality compliance checking system based on the knowledge graph analysis results and the structured query language (SQL) built-in rule knowledge base. Based on the knowledge base, the BIM model is automatically checked for quality compliance, and the naming, material, parameters, and rationality of each professional component are checked. Next, it is checked whether it conforms to the standard. Finally, the model modification plan is provided by checking the problem components and noncompliance details. The case application results show that the BIM model quality compliance checking system developed based on knowledge graph technology can identify defects or errors that do not meet the standards in the model, verify the feasibility and practicability of the system, and provide a new method for BIM designers to modify and improve the model.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
Acknowledgments
The authors sincerely acknowledge the editors and anonymous reviewers for their valuable comments and constructive suggestions, which considerably improved the exposition of this work. The authors also gratefully acknowledge those who provided data and suggestions. This work was funded by the Graduate Innovation Program of China University of Mining and Technology (Project No. 2023WLJCRCZL064), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Project No. KYCX23_2878), the National Natural Science Foundation of China (Grant No. 72171224), and the Humanities and Social Sciences Foundation of China’s Education Ministry (Grant No. 19YJAZH122).
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Journal of Computing in Civil Engineering
Volume 39 • Issue 1 • January 2025
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© 2024 American Society of Civil Engineers.
History
Received: Jan 17, 2024
Accepted: Jul 9, 2024
Published online: Oct 17, 2024
Published in print: Jan 1, 2025
Discussion open until: Mar 17, 2025
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