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Measurement and Modeling Needs for Next-Generation Concrete Binders



Jeffrey W. Bullard, Edward J. Garboczi, Paul E. Stutzman, Pan Feng, Alexander S. Brand, John G. Hagedorn, Wesley N. Griffin, Judith E. Terrill


Cementitious binders have become increasingly complex in their variety, mineralogical and chemical compositions, and structure over the past several decades, and there is no sign that the trend will stop or reverse in the future. Strategies for handling the complexity fall into one of two categories: (1) increasingly laborious trial-and-error exploration of the design space and mixture qualification process, or (2) systematic development of structure- property relationships, through curation of fundamental material component data and validated modeling based on fundamental scientific principles. The latter approach requires a sustained research investment and has received comparatively little attention in the concrete materials community. Nevertheless, the long-term benefits of determining structure-property relationships arguably far outweigh the costs. This paper describes general measurement and modeling strategies for developing structure-property relationships, and suggests how the existence of comprehensive materials data repositories and scientific modeling tools could revolutionize concrete materials design, including design for service life.
Cement and Concrete Composites


cement, concrete, computer modeling, materials characterization, machine learning


Bullard, J. , Garboczi, E. , Stutzman, P. , Feng, P. , Brand, A. , Hagedorn, J. , Griffin, W. and Terrill, J. (2017), Measurement and Modeling Needs for Next-Generation Concrete Binders, Cement and Concrete Composites (Accessed July 14, 2024)


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Created July 1, 2017, Updated September 19, 2017