| Title |
Applying Novelty Detection for Checking the Integrity of BIM Entity to IFC Class Associations |
| Authors |
Koo, Bonsang ; Shin, Byungjin |
| DOI |
http://dx.doi.org/10.6106/KJCEM.2017.18.6.078 |
| Keywords |
BIM ; IFC ; Anomaly Detection ; Novelty Detection ; one-class SVM |
| Abstract |
With the growing use of BIM in the AEC industry, various new applications are being developed to meet these specific needs. Such developments have increased the importance of Industry Foundation Classes, which is the international standard for sharing BIM data and thus ensuring interoperability. However, mapping individual BIM objects to IFC entities is still a manual task, and is a main cause for errors or omissions during data transfers. This research focused on addressing this issue by applying novelty detection, which is a technique for detecting anomalies in data. By training the algorithm to learn the geometry of IFC entities, misclassifications (i.e., outliers) can be detected automatically. Two IFC classes (ifcWall, ifcDoor) were trained using objects from three BIM models. The results showed that the algorithm was able to correctly identify 141 of 160 outliers. Novelty detection is thus suggested as a competent solution to resolve the mapping issue, mainly due to its ability to create multiple inlier boundaries and ex ante training of element geometry. |