Zirkular-X

Existing buildings hold significant potential for resource conservation and circular economy strategies, yet this potential is still far from being systematically exploited. A key barrier is the lack of complete, structured, and digitally accessible information on building components, materials, and technical building systems. As a result, dismantling, reuse, and recycling processes remain difficult to plan, and circular decision-making still requires substantial manual effort.

ZIRKULAR-X explores and develops how these deficits can be overcome through digital technologies. The project aims to create an AI-supported platform that enables automated capture, semantic linking, and circularity assessment of existing buildings. To achieve this, digital product passports, semantic knowledge graphs, and artificial intelligence methods are combined to identify, document, and evaluate materials, building components, and technical building services in a structured and scalable way.

Methodologically, the project builds on multimodal data analysis, semantic data modelling, and interoperable interfaces. Computer vision and natural language processing are used to extract information from plans, photographs, and technical documents, while knowledge graphs and ontologies connect these data with regulatory, ecological, and economic criteria. The resulting concepts are prototypically validated in real pilot buildings and transferred into a scalable platform architecture for assessing reuse, recycling, and deconstruction strategies.

Funded within the program "GreenTech Innovationswettbewerb – Digitale Technologien als Hebel für die Kreislaufwirtschaft" by the Federal Ministry for Research, Technology and Space, Germany.