Artificial intelligence can improve the quality assessment of recycled aggregates in construction and demolition waste streams, and the Reincarnate demonstration in Wuhan explores that in a new way.
Conducted in a real operational context, the case addresses one of the key barriers to circular construction: the variability and uncertainty associated with secondary raw materials.
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At the core of the approach is a neural network model trained to estimate key material properties, such as density, water absorption, and impurity content, based on image data. By analysing visual features of aggregate samples, the system predicts their quality and assigns them to suitable reuse categories. This enables faster and more consistent classification compared to traditional laboratory-based methods, which are typically time-consuming and resource-intensive.
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The results show that the model can successfully capture correlations between visual characteristics and material performance, achieving promising accuracy across multiple parameters. While further validation is still required, the system demonstrates strong potential for supporting real-time decision-making in recycling facilities and improving confidence in recycled materials.
By enabling more reliable and scalable quality assessment, the demonstration contributes to increasing the uptake of recycled aggregates and strengthening circular value chains in construction.