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http://dx.doi.org/10.13161/kibim.2022.12.2.012

Advanced Approach for Performance Improvement of Deep Learningbased BIM Elements Classification Model Using Ensemble Model  

Kim, Si-Hyun (서울과학기술대학교 건설시스템공학과)
Lee, Won-Bok (서울과학기술대학교 건설시스템공학과)
Yu, Young-Su (서울과학기술대학교 건설시스템공학과)
Koo, Bon-Sang (서울과학기술대학교 건설시스템공학과)
Publication Information
Journal of KIBIM / v.12, no.2, 2022 , pp. 12-25 More about this Journal
Abstract
To increase the usability of Building Information Modeling (BIM) in construction projects, it is critical to ensure the interoperability of data between heterogeneous BIM software. The Industry Foundation Classes (IFC), an international ISO format, has been established for this purpose, but due to its structural complexity, geometric information and properties are not always transmitted correctly. Recently, deep learning approaches have been used to learn the shapes of the BIM elements and thereby verify the mapping between BIM elements and IFC entities. These models performed well for elements with distinct shapes but were limited when their shapes were highly similar. This study proposed a method to improve the performance of the element type classification by using an Ensemble model that leverages not only shapes characteristics but also the relational information between individual BIM elements. The accuracy of the Ensemble model, which merges MVCNN and MLP, was improved 0.03 compared to the existing deep learning model that only learned shape information.
Keywords
BIM; IFC; Semantic Enrichment; Ensemble; Relational Information;
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