딥러닝 기반 BIM(Building Information Modeling) 벽체 하위 유형 자동 분류 통한 정합성 검증에 관한 연구

• 영문명: Using Deep Learning for automated classification of wall subtypes for semantic integrity checking of Building Information Models
• 발행기관: 한국BIM학회
• 저자명: 정래규(Jung, Rae-Kyu) 구본상(Koo, Bon-Sang) 유영수(Yu, Young-Su)
• 간행물 정보: 『KIBIM Magazine』9권 4호, 31~40쪽, 전체 10쪽
• 주제분류: 공학 > 건축공학
• 파일형태: PDF
• 발행일자: 2019.12.31

영문 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.

목차

1. 서 론

2. 이론적 고찰

3. 데이터 수집 및 딥러닝 모델 구축

4. 결과

5. 결론

키워드

BIM(BIM) IFC(IFC) 하위 유형 분류(Subtype Classification) 건축 벽체(Architectural Walls) 딥러닝(Deep Learning)