A method of optimizing high-performance concrete mix proportioning for a given workability and compressive strength using artificial neural networks and nonlinear programming is described. The basic procedure of the methodology consists of three steps: (1) Build accurate models for workability and strength using artificial neural networks and experimental data; (2) incorporate these models in software allowing an evaluation of the specified properties for a given mix; and (3) incorporate the software in a nonlinear programming package allowing a search of the optimum proportion mix design. For performing optimum concrete mix design based on the proposed methodology, a software package has been developed. One can conduct mix simulations covering all the important properties of the concrete at the same time. To demonstrate the utility of the proposed methodology, experimental results from several different mix proportions based on various design requirements are presented.

This paper investigates the potential of using design of experiments and neural networks to determine the effect of fly ash replacements, from 0 to 50%, on early and late compressive strength, from 3 to 56 days , of low- and high-strength concrete, at water-cementitious material ratios in the range of 0.3–0.7. The research reported in this paper shows the following conclusions: (1) using a simplex-centroid mixture experiment design, a much smaller number of experiments need to be performed to obtain meaningful data; (2) high correlations between the compressive strength and the component composition of concrete can be developed using the generalization capabilities of the neural networks; (3) analyses of variance to test the effects of the variables and their interactions on concrete strength can be performed; (4) the strength ratio, i.e., the percentage of strength of concrete containing fly ash to strength of concrete without fly ash (pure-cement concrete) based on the same w∕b and the same age, is sign...