Abstract
► Fly ash, slag and SF were incorporated to produce HPC. ► Formulation of optimal mix design for HPC is reported. ► Results of strength and rheology of concrete mixes are reported. ► Models presented provide reasonable predictions of properties under investigation. ► ANN was used to establish its applicability for the prediction of mix proportions.
High performance concrete (HPC) is defined in terms of both strength and durability performance under anticipated environmental conditions. HPC can be manufactured involving up to 10 different ingredients whilst having to consider durability properties in addition to strength. The number of ingredients and the number of properties of HPC, which needs to be considered in its design, are more than those for ordinary concrete. Therefore, it is difficult to predict the mix proportions and other properties of this type of concrete using statistical empirical relationship. An alternative approach is to use an artificial neural network (ANN). Based on the experimentally obtained results, ANN has been used to establish its applicability to the prediction and optimization of mix proportioning for HPC. It was demonstrated that mix proportioning for HPC can be predicted using ANN. However, some trial mixes are necessary for better performance and elimination of material variability factors from place to place. ANN procedure provides guidelines to select appropriate material proportions for required strength and rheology of concrete mixes and will reduce the number of trial mixes.