딥러닝 기반 BIM 부재 자동분류 학습모델의 성능 향상을 위한 Ensemble 모델 구축에 관한 연구

• 영문명: Advanced Approach for Performance Improvement of Deep Learningbased BIM Elements Classification Model Using Ensemble Model
• 발행기관: 한국BIM학회
• 저자명: 김시현 이원복 유영수 구본상
• 간행물 정보: 『KIBIM Magazine』12권 2호, 12~25쪽, 전체 14쪽
• 주제분류: 공학 > 건축공학
• 파일형태: PDF
• 발행일자: 2022.06.30

영문 초록

To increase the usability of Building Information Modeling (BIM) in construction projects, it is critical to ensure the interoperability of data between heterogeneous BIM software. The Industry Foundation Classes (IFC), an international ISO format, has been established for this purpose, but due to its structural complexity, geometric information and properties are not always transmitted correctly. Recently, deep learning approaches have been used to learn the shapes of the BIM elements and thereby verify the mapping between BIM elements and IFC entities. These models performed well for elements with distinct shapes but were limited when their shapes were highly similar. This study proposed a method to improve the performance of the element type classification by using an Ensemble model that leverages not only shapes characteristics but also the relational information between individual BIM elements. The accuracy of the Ensemble model, which merges MVCNN and MLP, was improved 0.03 compared to the existing deep learning model that only learned shape information.

목차

1. 서 론

2. 선행 연구 및 이론 고찰

3. 연구 방법

4. MVCNN

5. MLP

6. 앙상블

7. 종합 결과

8. 결론

감사의 글

References

키워드

빌딩정보모델 시멘틱 강화 앙상블 관계정보 BIM IFC Semantic Enrichment Ensemble Relational Information