Making it easier to recycle your house

According to Statistics Norway, an average of approximately 1100 detached houses have been demolished each year in Norway over the course of the past decade. However, only 7 per cent of the wood from these buildings was recycled.

Georgios Triantafyllidis, a PhD research fellow at the Norwegian University of Science and Technology (NTNU) in Gjøvik, believes a lot more could be recycled.

“Currently, the construction sector consumes a lot of raw materials and also generates large amounts of waste. This not only makes the industry a major consumer of new natural resources, but also one of the largest sources of global CO₂ emissions,” explained the researcher.

If we continue along the same path, the climate and environmental impact is expected to only increase as more and more buildings are renovated and upgraded.

“However, if we improve our reuse of building materials, these figures could look quite different,” said Triantafyllidis.

Extremely limited knowledge

Therefore, together with Professor Lizhen Huang and other colleagues at NTNU, he has developed a model designed to make it easier to categorize and calculate the amount of building materials available for reuse in our homes.

“If we are to transition to a more circular economy, we must first gain an overview of how much material is actually available and what its quality is. We currently know very little about this, especially when it comes to buildings,” said Huang.

Don’t judge a book by its cover

One way to acquire this knowledge is to analyze the composition of materials and calculate the quantities manually. However, it is a very costly and time-consuming process – not least when mapping entire cities.

One proposed solution has therefore been to use 3D scanning technology and machine learning. It significantly automates the process, but also comes with its own set of challenges.

“There is already commercially available technology that can scan a building and create a 3D representation of it. The problem is that these types of representations only provide information about what is visible from the outside. They do not reveal anything about the materials hidden behind, and even less about their condition or the quantities involved,” explained Huang.

It is also the case that these tools often misinterpret the data, adds Triantafyllidis:

“Since the approach uses visual data, problems often arise when something appears to be made of a certain material but is actually made of something completely different.

In practice, this means there is no reliable way to conduct large-scale mapping of the materials present in existing buildings.”

That is, until now.

Fragments become an advanced model

The method developed by the NTNU researchers is based on Building Information Modelling, or BIM.It is a process in which digital 3D models of buildings are created. These models often include many additional details, for example about the dimensions and quality of the materials used, as well as associated technical installations.

The advantages of having one common model that all involved parties can relate to have made BIM an established standard in major construction projects. However, since BIM is relatively new, there are no such models for the majority of the existing building stock.

Therefore, the researchers wondered whether it would be possible to use architectural and floor plans, technical specifications, land registries, regulations, photographic material and other available information to automate the process of creating it.

If so, it would be a solution that saves both time and money, explained Triantafyllidis:

“Instead of relying on expensive equipment and a range of experts and specialists, our method is based on information that already exists. Individually, this information may not be very valuable – a building regulation here, an architectural drawing there – but when we put the pieces of the puzzle together, a picture suddenly emerges that is far more comprehensive.

95% accurate

Initially, the researchers focused on whether the method is capable of calculating the amount of material in a building. In order to investigate this, they based their study on a fairly typical Norwegian detached house from the 1980s, with a footprint of 140 square metres.

Firstly, they manually created a calibration model of the building type. They then fed the model with all the available information they could find. The BIM model then automatically took shape based on the data, providing the researchers with a much more detailed model of the house.

The experiment showed that the method was capable of calculating the quantities of material present in the exterior walls and roof with an accuracy of 95 per cent. However, they emphasize that further experiments are needed to confirm the model’s accuracy with certainty. But one thing is clear, the results are promising.

And that is not all:

“Since it is easy to adapt and based on data available for virtually all buildings, the method can also be easily scaled up if needed,” said Triantafyllidis.

Renovation updates

Despite the promising results, the researchers are still looking for ways to make the method even better and more applicable. One way may be to include information directly from homeowners.

Many Norwegian homes have been significantly altered since they were built, but not all changes have been registered. Since their method is largely based on publicly available documentation, it can lead to inaccuracies.

“Renovation can involve everything from minor cosmetic changes to major structural interventions. In many cases, it will not make a difference. But let us say that major interior walls are demolished – the quantities of material could then change without our method necessarily detecting it,” said Triantafyllidis.

The researchers therefore envision a solution where homeowners themselves register any major changes they make to their homes.

“By including this type of information from homeowners, the models would always be up-to-date and provide a very accurate overview of which materials are potentially available for reuse,” he said.

The researchers emphasize that this is an important prerequisite for achieving a well-functioning circular economy.

Reference: Triantafyllidis, G., Müller, D. B., Wellinger, S., & Huang, L. (2025). Accelerating Circular Cities with Semi-Automatic Building Information Modeling for Existing Buildings. Journal of Cleaner Production, 145783. https://doi.org/10.1016/j.jclepro.2025.145783