A new international study has unveiled a pioneering reference architecture and data framework that promises to revolutionise how digital twins are implemented during the construction execution phase—long considered the final frontier in BIM-driven lifecycle management
Led by researchers from the Technical University of Munich and Technion – Israel Institute of Technology, and supported by teams from the University of Cambridge and Inria France, the paper introduces a semantically rich, plug-in-ready digital twin platform architecture specifically designed for complex, real-time site environments.
Published in the journal Automation in Construction, the research provides the construction sector with:
- A reference architecture optimised for managing raw data, construction intent, and site status in parallel
- A newly developed Digital Twin Construction Ontology (DTC Ontology) that supports detailed progress tracking and plan-vs-actual comparisons
- Proven validation via the ConSLAM case study, utilising point cloud data from a major London redevelopment project
“Our goal was to close the gap between digital planning and physical site execution,” said lead author Dr. Jonas Schlenger, chair of Computational Modeling and Simulation at TUM.
“By combining RDF-based semantic modelling with modular platform design, we’ve laid a foundation for scalable, interoperable digital twins tailored to the real needs of construction professionals.”
Addressing the core challenge: Execution intelligence
While digital twins have gained traction in facility management and energy optimisation, real-time site monitoring and execution intelligence remain underdeveloped. The new framework introduces an ontology-driven system that separates project intent (what was planned) from project status (what was built), allowing teams to:
- Detect delays and deviations in real time
- Improve co-ordination across subcontractors
- Calculate performance KPIs dynamically
- Reduce waste, errors, and rework
The platform architecture enables third-party services — such as AI-based defect detection or 4D progress analytics — to plug in securely via REST APIs, ensuring data schema compliance through SHACL validation.
A scalable blueprint for Digital Twin Maturity
The system is designed to scale from small contractors to major infrastructure projects. Key innovations include:
- RDF graph architecture integrated with time-series and object databases
- Seamless geometry handling with PLY and GeoSPARQL
- Support for industry ontologies like IFC, BOT, SOSA/SSN, and SAREF4City
- A centralised yet flexible platform architecture based on microkernel design principles
The research forms part of the EU Horizon 2020-funded BIM2TWIN project, a major European initiative to deliver a next-generation Digital Building Twin Platform for the construction industry.
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