Virtual Testing of Cement and Concrete

Published

May 10, 2006

Author(s)

Dale P. Bentz, Edward J. Garboczi, Jeffrey W. Bullard, Chiara F. Ferraris, Nicos Martys, Paul E. Stutzman

Abstract

Concrete is a unique multi-phase, random, complex, and composite material that provides both strength and impermeability to engineered structures. Unlike most construction materials, the properties of concrete continue to develop over time and in place an aid in processing, but a complication in testing and quality assurance. This is clearly exemplified by the bread-and-butter test of concrete quality compliance, 28-day compressive strength. Having to wait 28 days to assure performance compliance is another unique feature of concrete, and not necessarily an attractive one. This holds true both for the field engineer who would prefer to directly proceed with further aspects of the construction, and the industrial or academic researcher who would prefer to avoid waiting 28 days (or more) before truly knowing the effects of a new chemical additive or an alternative processing methodology. Thus, many efforts have been made to predict (and ensure) concrete performance based on waiting periods of less than 28 days.One area of effort has been the development of computer models to predict cement and concrete properties via virtual testing. In these models, typically, starting materials are characterized in some fashion and this information is input into one or more computer models to predict a variety of properties of the fresh and hardening cement or concrete. If properties can be successfully predicted, the time and cost savings to the cement and concrete industry can be tremendous.

Citation

American Society for Testing and Materials

Pub Type

Journals

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Keywords

cement, computer models, concrete, prediction, quality compliance, virtual testing

Citation

Bentz, D. , Garboczi, E. , Bullard, J. , Ferraris, C. , Martys, N. and Stutzman, P. (2006), Virtual Testing of Cement and Concrete, American Society for Testing and Materials, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=860546 (Accessed December 26, 2024)