HBIM model of part of the St. Wenceslas Church in Ostrava

Abstract

The digital preservation of cultural heritage increasingly relies on Heritage Building Information Modeling (HBIM). However, standard architectural software is engineered for modern, straight line construction, which often struggles to handle the complex geometric irregularities and complex layered historical data of cultural monuments. To bridge this gap, this diploma thesis directly addresses a highly repeatable, open-source framework for semantic HBIM by using the St. Wenceslas Church in Ostrava as a primary case study, the research validates an practical and end-to-end workflow from reality capture to semantic data federation.

The physical environment of the church's Main Hall was captured using Terrestrial Laser Scanning (Leica RTC360) by providing a millimeter-accurate spatial baseline. During the geometric reconstruction in Autodesk Revit, using a hybrid Level of Geometry (LOG) strategy under U.S. Institute of Building Documentation (USIBD) tolerances (±20 mm). In order to overcome software limitations without reducing the computational performance, complex historical anomalies, like deformed Gothic cross-ribbed vaults and leaning Renaissance pillars, were parametrized using custom swept and loadable families.

In order to ensure the 3D model functions as a true historical database rather than just a static drawing, this geometry was semantically enriched using the internationally recognized CIDOC CRM (ISO 21127:2023) ontology. The Protégé ontology editor was used strictly as a theoretical blueprint to bypass the limitations of disconnected external ontology files, which uses knowledge graphs to link events, phases, actors, materials and more. The historical metadata was embedded directly into the 3D elements through custom Shared Parameters and exported using the advanced IFC 4x3 schema. After this, the enriched openBIM dataset is uploaded into the CRAIS.io web viewer which validates the creation of an active, interactive digital conservation logbook, therefore it validates a successful visualization and data retrieval. Ultimately, this thesis proves that complex architectural heritage can be digitally managed using open standards without sacrificing geometric fidelity and successfully laying the foundation for future integration with IoT sensors and Machine Learning (ML) as a living Digital Twin.