SOURCE-LINKED INTELLIGENCE
Concrete products through sustainable and innovative carbon-storing binders
optimizing the accelerated mineral carbonation process, refining the formulations and performance of these mineralized SCMs, and validating their suitability for large-scale industrial applications. Machine learning models and advanced simulation tools will be employed to design sustainable concrete mixtures and evaluate their performance. The ultimate goal is to reduce the embodied carbon emissions of concrete, while maintaining or improving its structural performance. By leveraging interdisciplinary expertise, we aim to develop a commercially viable binder with lower embodied emissions for the construction industry. This project aligns with the EIC Pathfinder Challenge objectives and contributes to the European Green Deal, Fit for 55, and Circular Economy Action Plan, supporting the EU’s broader mission of achieving carbon neutrality and advancing sustainable building practices. CO2 Sequestration, Mineral Carbonation, Accelerated Mineralization, Low-Carbon Cement, Supplementary Cementitious Materials (SCMs), Machine Learning in Concrete Design.
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 3997434.44
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T04:21:15.460Z. This is not the publication date.