KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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Modeling Element Relations as Structured Graphs Via Neural Structured Learning to Improve BIM Element Classification

Neural Structured Learning 기반 그래프 합성을 활용한 BIM 부재 자동분류 모델 성능 향상 방안에 관한 연구

  • Yu, Youngsu (Seoul National University of Science and Technology) ;
  • Lee, Koeun (Seoul National University of Science and Technology) ;
  • Koo, Bonsang (Seoul National University of Science and Technology) ;
  • Lee, Kwanhoon (Korea University)
  • 유영수 (서울과학기술대학교 건설시스템공학과) ;
  • 이고은 (서울과학기술대학교 건설시스템공학과) ;
  • 구본상 (서울과학기술대학교 건설시스템공학과) ;
  • 이관훈 (고려대학교 컴퓨터학과)
  • Received : 2020.11.06
  • Accepted : 2020.12.29
  • Published : 2021.06.01
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Abstract

Building information modeling (BIM) element to industry foundation classes (IFC) entity mappings need to be checked to ensure the semantic integrity of BIM models. Existing studies have demonstrated that machine learning algorithms trained on geometric features are able to classify BIM elements, thereby enabling the checking of these mappings. However, reliance on geometry is limited, especially for elements with similar geometric features. This study investigated the employment of relational data between elements, with the assumption that such additions provide higher classification performance. Neural structured learning, a novel approach for combining structured graph data as features to machine learning input, was used to realize the experiment. Results demonstrated that a significant improvement was attained when trained and tested on eight BIM element types with their relational semantics explicitly represented.

IFC 정보의 시멘틱 무결성 확보를 위해 BIM 부재와 IFC 엔티티 간 매핑 검증이 필요하다. 이와 관련된 기존 연구들은 기하정보 기반으로 학습시킨 기계학습 알고리즘을 활용하여 BIM 부재 인식 및 분류를 통해 매핑 검증을 실시하였으나, 유사한 기하특성을 가진 부재를 구분하지 못한다는 한계점이 존재하였다. 이에 본 연구는 BIM 모델의 주요 부재를 인공신경망 기반으로 자동 분류하되, 부재 간 관계정보를 삽입하여 분류성능을 향상시키는 것을 목적으로 하였다. 이를 위해 기존 특성 외에 구조화된 신호를 함께 학습하는 NSL 프레임워크를 활용하여 8개의 BIM 부재를 분류하는 모델을 구축하였으며, 그 결과 기하정보 기반 인공신경망 모델과 대비하여 부재 간 관계정보를 삽입한 NSL 모델의 분류정확도가 현저히 상승한 것을 확인하였다.

Keywords

Acknowledgement

본 연구는 국토교통부 도시건축 연구개발사업의 연구비지원(21AUDP-B127891-05)에 의해 수행되었습니다. 본 논문은 2020 CONVENTION 논문을 수정·보완하여 작성되었습니다.

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