Fraunhofer Institute for Computer Graphics Research IGDHolistic process for sustainable, modular, and circular building renovation – BAU-DNS
The construction industry in Germany faces a significant challenge in light of the climate policy goals set by the federal government and the EU. Around 40 percent of the country's CO₂ emissions stem from the construction and operation of buildings. Renovation, particularly of existing buildings, therefore presents a crucial lever for reducing emissions.
However, the current status quo makes it difficult to respond quickly. There is a lack of sufficient skilled trades and specialists to significantly increase the renovation rate. Additionally, renovations are hindered by varying requirements depending on the building, outdated construction plans, and complex structures. High costs, which only amortize over a very long period through energy savings, also deter renovation efforts.
To effectively reduce emissions in the construction sector, an efficient, cost-saving, and sustainable approach to building renovation is required.
One potential solution is the BAU-DNS flagship project, which aims to improve the productivity and cost efficiency of facade renovations. The goal is to establish a solid foundation that enables the entire life cycle of a facade renovation to be planned and executed sustainably from start to finish. The project examines the planning process and is based on the standard service phases defined in the HOAI (Official Scale of Fees for Services by Architects and Engineers). It demonstrates how the development and efficient use of digital models across all renovation steps—within a continuous and integrated workflow—can lead to a significant increase in productivity. At the same time, the use of newly developed methods allows for greater automation and efficiency in data processing.
The Fraunhofer IGD focuses on designing a production-driven, modular Digital Twin for resource-efficient and cost-effective planning, execution, manufacturing, and operation in building renovation. To achieve this, an information model is first developed to derive all relevant building data for facade renovation. AI-based methods are being developed to enable the automated transformation of existing building data into the Digital Twin’s data model. A scalable infrastructure and the provision of the developed algorithms as smart services ensure a flexible and efficient implementation of the processes, supporting sustainable and circular renovations.
