Database-driven IFC framework for digital twins of existing structures

Industry Foundation Classes (IFC) is an open standard for representing geometric and semantic information in building information modeling (BIM), which is widely used to support the development of digital twins (DTs). However, IFC has limited capabilities for handling heterogeneous operational data, hindering the integration of DTs into workflows such as structural health monitoring (SHM). This paper presents a database-driven IFC framework that stores IFC models and semantic data in a structured database. The IFC representation is extended to incorporate SHM and inspection information, enabling traceable associations between monitoring records and their corresponding IFC components, with data access provided through a RESTful API. The framework is validated using an office building and a simply supported bridge. Benchmarks demonstrate the relational approach’s efficiency in global counts, property retrieval, and sensor-oriented queries. The framework has the potential to provide a unified, extensible data foundation for lifecycle-oriented digital twins of existing structures.