A Semantic Scan-to-IFC Pipeline for Automated Generation of BEM-Ready Building Models from Mobile Indoor Scanning Data

Building Energy Modelling (BEM) for existing buildings is constrained by the lack of reliable as-built Building Information Models (BIMs) and persistent BIM-to-BEM interoperability problems. This study proposes a semantic scan-to-Industry Foundation Classes (IFC) workflow that converts mobile indoor scans into a simplified IFC model for BEM preprocessing. Apple RoomPlan captures room-scale building elements, which are exported as JSON and converted into IFC 4×3 ADD2 using a Python-based converter. To address partial scans, the workflow generates closed analytical volumes, inferred walls and ceiling slabs, and metadata distinguishing measured from reconstructed geometry. It then automatically generates IfcSpace entities and IfcRelSpaceBoundary2ndLevel relationships. The workflow was evaluated using a historic university building. For the selected case-study area, processing from mobile scanning to initial VICUS Buildings import required 21 min, excluding subsequent manual verification of boundary conditions and assignment of thermophysical properties. Under identical construction stratigraphies and usage profiles, the scan-derived model produced a total heating-season demand 5.8% higher than the Revit reference model. These results indicate that partial semantic indoor scans can support the rapid preparation of structured IFC models for preliminary BEM applications.