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

정래규; 구본상; 유영수

doi:10.13161/kibim.2019.9.4.031

[국내논문] 딥러닝 기반 BIM(Building Information Modeling) 벽체 하위 유형 자동 분류 통한 정합성 검증에 관한 연구
Using Deep Learning for automated classification of wall subtypes for semantic integrity checking of Building Information Models 원문보기

Journal of KIBIM = 한국BIM학회논문집, v.9 no.4, 2019년, pp.31 - 40

정래규 (서울과학기술대학교 건설시스템공학과) , 구본상 (서울과학기술대학교 건설시스템공학과) , 유영수 (서울과학기술대학교 건설시스템공학과)

Abstract ▼ AI-Helper

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.

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표/그림 (15)

그림 Figure 1. MVCNN architecture(Su et al., 2015)
그림 Figure 2. PointNet applications(Qi et al., 2017)
그림 Figure 3. PointNet architecture(Qi et al., 2017)
그림 Figure 4. Flow Chart
표 Table 1. BIM model overview
그림 Figure 5. Deep network architecture for MVCNN
그림 Figure 6. Deep network architecture for PointNet
표 Table 2. Wall element type classification
표 Table 3. Wall element training and test set (7:3)
그림 Figure 7. Precision/Recall curve
그림 Figure 8. ROC curve
표 Table 4. Accuracy (ACC) results for SVM, Multi-view CNN, PointNet (IfcWall)
표 Table 5. SVM classification results (confusion table)
표 Table 6. MVCNN classification results (confusion table)
표 Table 7. PointNet classification results (confusion table)

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