Korean Journal of Construction Engineering and Management (한국건설관리학회논문집)

Korea Institute of Construction Engineering and Management (한국건설관리학회)
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Development of Graph based Deep Learning methods for Enhancing the Semantic Integrity of Spaces in BIM Models

BIM 모델 내 공간의 시멘틱 무결성 검증을 위한 그래프 기반 딥러닝 모델 구축에 관한 연구

  • Lee, Wonbok (Department of Civil Engineering, Seoul National University of Science and Technology) ;
  • Kim, Sihyun (Department of Civil Engineering, Seoul National University of Science and Technology) ;
  • Yu, Youngsu (Department of Civil Engineering, Seoul National University of Science and Technology) ;
  • Koo, Bonsang (Department of Civil Engineering, Seoul National University of Science and Technology)
  • 이원복 (서울과학기술대학교 건설시스템공학과) ;
  • 김시현 (서울과학기술대학교 건설시스템공학과) ;
  • 유영수 (서울과학기술대학교 건설시스템공학과) ;
  • 구본상 (서울과학기술대학교 건설시스템공학과)
  • Received : 2022.01.27
  • Accepted : 2022.03.24
  • Published : 2022.05.31
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Abstract

BIM models allow building spaces to be instantiated and recognized as unique objects independently of model elements. These instantiated spaces provide the required semantics that can be leveraged for building code checking, energy analysis, and evacuation route analysis. However, theses spaces or rooms need to be designated manually, which in practice, lead to errors and omissions. Thus, most BIM models today does not guarantee the semantic integrity of space designations, limiting their potential applicability. Recent studies have explored ways to automate space allocation in BIM models using artificial intelligence algorithms, but they are limited in their scope and relatively low classification accuracy. This study explored the use of Graph Convolutional Networks, an algorithm exclusively tailored for graph data structures. The goal was to utilize not only geometry information but also the semantic relational data between spaces and elements in the BIM model. Results of the study confirmed that the accuracy was improved by about 8% compared to algorithms that only used geometric distinctions of the individual spaces.

BIM의 도입에 따라 공간이 개별 객체로 인식되면서 객체화된 공간의 속성정보는 법규검토, 에너지 분석, 피난 경로 분석 등을 위한 기반 데이터로 사용 가능하기에 BIM의 활용성을 넓힐 수 있는 발판을 마련하였다. 그러나 BIM 모델 내 개별 공간 속성의 오기입이나 누락이 없는 시멘틱 무결성(semantic integrity)이 보장되어야 하는데, 다수의 참여자에 의한 수작업으로 진행되는 BIM 모델링 과정 특성 상 설계 오류가 빈번히 발생한다는 문제점이 존재한다. 이를 해결하기 위해 BIM 모델의 공간 정합성 검증을 위한 연구가 다수 진행되었으나, 적용 범위가 한정적이거나 분류 정확도가 낮은 한계점이 존재하였다. 본 연구에서는 공간의 기하정보 뿐 아니라 BIM 모델 내 공간과 부재 간 연결 관계를 Graph Convolutional Networks (GCN) 학습과정에 활용하여 향상된 성능의 공간 자동 분류모델을 구축하고자 하였다. 구축된 GCN 기반 모델의 성능을 공간의 기하정보만으로 학습된 기계학습 모델인 Multi-Layer Perceptron (MLP)과 비교하여 공간 분류 시 연결 관계 적용의 효용성을 검증하고자 하였다. 이를 통해 관계정보 활용 시 약 8% 내외 수준으로 공간 분류 성능이 향상되는 것으로 확인되었다.

Keywords

Acknowledgement

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임. (No.2020R1A2C1100741).

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