Automatic building feature detection and reconstruction in IFC models

Abstract

The flexibility of the Industry Foundation Classes (IFC) format makes it challenging to blindly rely on the data it stores. This is because, to allow its flexibility, the stored data in an IFC file can be missing, incorrect, redundant, or stored in vague ways while the file can still be considered format compliant. This missing reliability is an issue for a format that is so central to the Architecture, Engineering and Construction (AEC) industry. This also severely hampers the chances for automated processes to be applied in the AEC industry. Chances that could reduce the time and cost spend on a construction while enlarging its quality. Prior done research on this subject is sparse. The small subset of this research that covers the ”repair” or reliable extraction of IFC data primarily focuses on idealized situations that do not often occur in practice. Due to the combination of these factors, this master thesis attempts to discover if it is possible to extract reliable data from the unreliable IFC files with an automated method. Because of the large flexibility and broadness of the IFC format, the scope of the thesis had to be limited. This was done by focusing on the extraction/reconstruction of storeys (and their elevations), rooms, and apartments. For these three subjects not only extraction/reconstruction methods have been created, but they have also been implemented in an open source software tool1. To develop reliable processes, the processes have to be based upon reliable data. From the data available in the IFC format, the tangible geometry is considered to be the most reliable. However, not all the other data can be discarded. Only the data that is implicitly stored in the tangible geometry can be ignored without losing information. In practice this means that most, if not all, topologic data and a subset of the semantic data can be ignored. It was found that for the extraction/reconstruction of storeys (and their elevations), a z-value extraction based on created IfcSlab/IfcRoof groups was fairly effective. These groups were made based on their size, height, and location. This process does show a lowered reliability in buildings with complex storey structures. It was found that for the extraction/reconstruction of rooms, a voxelization approach functioned very well. The boolean refinement that followed, which took the shape from a rough voxelization to the precise room shape, showed some reliability issues. Finally, it was found that for the extraction/reconstruction of apartments that rules applied to a created space syntax graph showed promise. The rules that were used were simple, resulting in lower reliability in structures that had more complex room relationships. In conclusion, this research has been unable to give a definite answer to the issues at hand. However, all three the subjects that have been covered showed promising results and can be considered steps towards a solution.