Imagine if buildings had a digital 'ingredient list' that told you exactly how to take them apart without breaking everything. This project creates a smart system that uses robots and AI to automatically sort through rubble and debris. It turns old building waste into high-value materials instead of just throwing them in a landfill.
Construction and demolition waste is often poorly sorted and undervalued, leading to high landfill costs and wasted materials. Current demolition processes lack the data needed to recover materials efficiently.
A digital information management system for waste traceability, AI-assisted waste classification software, and robotic segregation hardware for off-site sorting.
If you are a waste processor dealing with low-quality recycled aggregates — this project developed AI-assisted classification and robotic segregation that increases the value of recovered materials.
If you are a design firm dealing with unpredictable demolition costs — this project developed a digital information management system that integrates waste predictors into building models to plan for reuse.
If you are a contractor dealing with inefficient site clearing — this project developed automated decision-making tools for repurposing and deconstruction to speed up the removal process.
Based on available project data, specific pricing for the tools is not provided, though the project received an EU contribution of EUR 6,091,958 for development.
The project is designed for industrial application, utilizing 6 demonstrators to evaluate the feasibility and efficiency of the robotic and digital solutions.
Based on available project data, specific licensing terms are not mentioned, but the project involves a consortium of 23 partners including 12 industry players.
The system integrates directly with Building Information Modeling (BIM) and uses digital twins to automate the conversion of construction information.
The project period runs from 2022-07-01 to 2026-06-30, indicating that full demonstration results will be available by mid-2026.
The project is heavily industry-driven with a 52% industry ratio, comprising 12 companies (including 10 SMEs) and 8 research/university partners. This strong commercial presence across 7 countries suggests the results are being developed for immediate market application rather than pure academic study.
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