BIM Library Transplant: Bridging Human Expertise and Artificial Intelligence for Customized Design Detailing
Publication: Journal of Computing in Civil Engineering
Volume 38, Issue 2
Abstract
This study introduces a framework for transplanting a building information modeling (BIM) library. Design detailing constitutes 50%–60% of the total design time, even within the BIM context. Previous studies have highlighted the potential of integrating BIM and artificial intelligence (AI) for enhanced productivity. However, challenges arise due to architects’ preferences for unique project-specific details when applying generalized AI approaches based on big data. To address this, we propose a BIM library transplant framework. This framework automatically identifies objects at a high level of development (LOD) from a selected existing BIM model (i.e., a donor model) and matches them with low-LOD objects in a new model (i.e., a recipient model). Subsequently, it replaces the low-LOD objects with corresponding high-LOD objects. The framework involves three steps: (1) extracting the library from the donor model, (2) matching the library, and (3) transplanting the library from the donor to recipient model. To validate its efficacy, we implemented the BIM library transplant framework as a Revit add-on, employing the random forest classifier as the object-matching AI model. Our results indicate that the implemented framework has the potential to reduce detailing time by approximately 60%–70%, while achieving an accuracy of 65%–80%.
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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 be provided only with restrictions. Donor models A, B, and C used for the validation in this study were provided by the architects involved in the experiment, and therefore are proprietary in nature.
Acknowledgments
The authors thank Inyoung Park, Hwanwoong Jeong, and Yumin Park of SAAI+ for providing their esteemed digital assets and the generous allotment of their time for the validation of the proposed framework. This work was supported in 2024 by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure, and Transport (Grant RS-2021-KA163269).
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Published In
Journal of Computing in Civil Engineering
Volume 38 • Issue 2 • March 2024
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© 2024 American Society of Civil Engineers.
History
Received: Aug 29, 2023
Accepted: Dec 4, 2023
Published online: Jan 9, 2024
Published in print: Mar 1, 2024
Discussion open until: Jun 9, 2024
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