한국BIM학회 논문집 (Journal of KIBIM)

  • 제12권2호
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  • Pages.12-25
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  • 2022
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  • 2288-1697(pISSN)
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  • 2288-1697(eISSN)
한국BIM학회 (Korean Institute of Building Information Modeling)
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딥러닝 기반 BIM 부재 자동분류 학습모델의 성능 향상을 위한 Ensemble 모델 구축에 관한 연구

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

  • 김시현 (서울과학기술대학교 건설시스템공학과) ;
  • 이원복 (서울과학기술대학교 건설시스템공학과) ;
  • 유영수 (서울과학기술대학교 건설시스템공학과) ;
  • 구본상 (서울과학기술대학교 건설시스템공학과)
  • 투고 : 2022.02.04
  • 심사 : 2022.05.10
  • 발행 : 2022.06.30
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초록

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.

키워드

과제정보

본 연구는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행되었습니다. (No. NRF-2020R1A2C1100741).

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