Christoph Emunds, Nicolas Pauen, Veronika Richter, Jérôme Frisch, Christoph van Treeck
Enhancing interoperability and information exchange between domain-specific software products for BIM is an important aspect in the Architecture, Engineering, Construction and Operations industry. Recent research started investigating methods from the areas of machine and deep learning for semantic enrichment of BIM models. However, training and evaluation of these machine learning algorithms requires sufficiently large and comprehensive datasets. This work presents IFCNet, a dataset of single-entity IFC files spanning a broad range of IFC classes containing both geometric and semantic information. Using only the geometric information of objects, the experiments show that three different deep learning models are able to achieve good classification performance.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| IFC Entity Classification | IFCNetCore | Balanced Accuracy | 85.54 | MVCNN |
| IFC Entity Classification | IFCNetCore | F1 Score | 86.93 | MVCNN |
| IFC Entity Classification | IFCNetCore | Balanced Accuracy | 83.32 | MeshNet |
| IFC Entity Classification | IFCNetCore | F1 Score | 85.72 | MeshNet |
| IFC Entity Classification | IFCNetCore | Balanced Accuracy | 79.11 | DGCNN |
| IFC Entity Classification | IFCNetCore | F1 Score | 82.15 | DGCNN |