한국BIM학회 논문집 (Journal of KIBIM)

  • 제9권4호
  • /
  • Pages.31-40
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  • 2019
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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(Building Information Modeling) 벽체 하위 유형 자동 분류 통한 정합성 검증에 관한 연구

Using Deep Learning for automated classification of wall subtypes for semantic integrity checking of Building Information Models

  • 정래규 (서울과학기술대학교 건설시스템공학과) ;
  • 구본상 (서울과학기술대학교 건설시스템공학과) ;
  • 유영수 (서울과학기술대학교 건설시스템공학과)
  • 투고 : 2019.11.19
  • 심사 : 2019.11.19
  • 발행 : 2019.12.31
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초록

With Building Information Modeling(BIM) becoming the de facto standard for data sharing in the AEC industry, additional needs have increased to ensure the data integrity of BIM models themselves. Although the Industry Foundation Classes provide an open and neutral data format, its generalized schema leaves it open to data loss and misclassifications This research applied deep learning to automatically classify BIM elements and thus check the integrity of BIM-to-IFC mappings. Multi-view CNN(MVCC) and PointNet, which are two deep learning models customized to learn and classify in 3 dimensional non-euclidean spaces, were used. The analysis was restricted to classifying subtypes of architectural walls. MVCNN resulted in the highest performance, with ACC and F1 score of 0.95 and 0.94. MVCNN unitizes images from multiple perspectives of an element, and was thus able to learn the nuanced differences of wall subtypes. PointNet, on the other hand, lost many of the detailed features as it uses a sample of the point clouds and perceived only the 'skeleton' of the given walls.

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참고문헌

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