SOURCE-LINKED INTELLIGENCE
Concrete matrices for high-cycle-fatigue resistant, eco-efficient infrastructure
team will be combined in a concerted application of physico-chemical modeling approaches of hydration processes and advanced methods of multiscale and multifield computational mechanics supported by machine learning and accompanied by a rigorous experimental validation program. The developed coherent methodical framework will include innovative theoretical and numerical as well as tailored experimental approaches covering all relevant spatial and temporal scales to enable realistic predictions of the fatigue behavior of existing and future eco-efficient concrete formulations. This is necessary to give design engineers confidence in the new materials, and to enable design concepts, optimally satisfying requirements for sustainable, economical, and reliable future transportation and energy infrastructure. high-cycle fatigue, eco-efficient concrete, thermodynamics, physical chemistry, multiscale modeling, probabilistic computational mechanics, structural engineering science, material characterization
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 9993698
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T03:21:21.440Z. This is not the publication date.