Journal of KIBIM (한국BIM학회 논문집)

Korean Institute of Building Information Modeling (한국BIM학회)
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Using Geometry based Anomaly Detection to check the Integrity of IFC classifications in BIM Models

기하정보 기반 이상탐지분석을 이용한 BIM 개별 부재 IFC 분류 무결성 검토에 관한 연구

  • 구본상 (서울과학기술대학교 건설시스템공학과) ;
  • 신병진 (서울과학기술대학교 건설시스템공학과)
  • Received : 2017.02.06
  • Accepted : 2017.03.03
  • Published : 2017.03.31
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Abstract

Although Industry Foundation Classes (IFC) provide standards for exchanging Building Information Modeling (BIM) data, authoring tools still require manual mapping between BIM entities and IFC classes. This leads to errors and omissions, which results in corrupted data exchanges that are unreliable and thus compromise the validity of IFC. This research explored precedent work by Krijnen and Tamke, who suggested ways to automate the mapping of IFC classes using a machine learning technique, namely anomaly detection. The technique incorporates geometric features of individual components to find outliers among entities in identical IFC classes. This research primarily focused on applying this approach on two architectural BIM models and determining its feasibility as well as limitations. Results indicated that the approach, while effective, misclassified outliers when an IFC class had several dissimilar entities. Another issue was the lack of entities for some specific IFC classes that prohibited the anomaly detection from comparing differences. Future research to improve these issues include the addition of geometric features, using novelty detection and the inclusion of a probabilistic graph model, to improve classification accuracy.

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References

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