Imagine if every building had a digital receipt listing every single material used, like a detailed grocery list for a house. This project uses AI and scanners to identify what's inside old buildings so we can treat them like warehouses of spare parts. Instead of smashing everything into a landfill, we can precisely pick out what to reuse for new projects.
Construction and demolition waste is the largest waste stream in the EU, causing high CO2 emissions and environmental damage. Companies lack a reliable way to identify and track materials in existing buildings for reuse.
AI-assisted identification software, digital building logbooks, material passports, and a C-BIM standard for interoperability.
If you are a demolition contractor dealing with massive amounts of construction waste — this project developed AI-assisted identification tools that turn debris into a catalog of reusable assets. This allows you to sell recovered materials instead of paying landfill fees.
If you are a BIM consultancy dealing with outdated building records — this project developed C-BIM standards and digital building logbooks. This enables you to provide clients with a precise material passport for their assets.
If you are a developer dealing with high renovation costs — this project developed a 5Re process (Reduce, Reuse, Repair, Recycle, Renovate) supported by digital banks. This helps you lower material procurement costs by sourcing from urban mining.
Based on available project data, specific pricing or cost structures for the software tools are not provided.
The project includes 3 demonstration pilots linked to actual construction projects to validate the methodology for real-world use.
Based on available project data, the specific IP and licensing terms are not detailed, though the project emphasizes open standards for interoperability.
Yes, it specifically targets the largest waste stream in the EU to reduce the amount of construction and demolition waste sent to landfills.
It improves the BIM standard to create C-BIM, ensuring it works with open standards and existing commercial databases.
The consortium is heavily industry-driven with a 59% industry ratio, comprising 10 industrial partners and 6 SMEs across 9 countries. This strong commercial presence, balanced by 2 universities and 3 research centers, suggests the project is focused on market adoption rather than purely academic research.
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