Probabilistic models for modulus of elasticity of self-consolidated concrete: Bayesian approach

Current models of the modulus of elasticity, E, of concrete recommended by the American Concrete Institute and the American Association of State Highway and Transportation Officials are derived for normally vibrated concrete (NVC). Because self-consolidated concrete (SCC) mixtures differ from NVC in the quantities and types of constituent materials, supplementary cementing materials, and chemical admixtures, the current models, may not take into consideration the complexity of SCC, and thus they may predict the E of SCC inaccurately. Although some authors recommend specific models to predict E of SCC, they include only a single variable of assumed importance, namely, the design compressive strength of concrete, f_c'. However, there are other parameters that may need to be accounted for while developing a prediction model for E of SCC. In this paper, a Bayesian variable selection method is used to identify the significant parameters in predicting the E of SCC, and more accurate models for E are generated using these variables. The models have a parsimonious parametrization for ease of use in practice and properly account for the prevailing uncertainties.