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

• 영문명: Using Geometry based Anomaly Detection to check the Integrity of IFC classifications in BIM Models
• 발행기관: 한국BIM학회
• 저자명: 구본상(Koo, Bonsang) 신병진(Shin, Byungjin)
• 간행물 정보: 『KIBIM Magazine』7권 1호, 1~10쪽, 전체 10쪽
• 주제분류: 공학 > 건축공학
• 파일형태: PDF
• 발행일자: 2017.03.30

영문 초록

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.

키워드

BIM IFC 기계학습 이상탐지분석 Building Information Modeling Industry Foundation Classes Machine Learning Anomaly Detection