Modeling Slump of Concrete Using the Artificial Neural Networks

This paper proposes the artificial neural networks (ANNs) and applies it to estimate the slump of high performance concrete (HPC). It is known that HPC is a highly complex material whose behavior is difficult to model, especially for slump. To estimate the slump, it is a nonlinear function of the content of all concrete ingredients, including cement, fly ash, blast furnace slag, water, super plasticizer, and coarse and fine aggregate. Therefore, slump estimation is set as a function of the content of these seven concrete ingredients and additional four important ratios. The ANNs algorithm presented in this paper has the advantage of processed the complicated multi-variable HPC slump estimation. The results show that ANNs is a powerful method for obtaining a more accurate prediction through learning procedures which outperforms the traditional multiple linear regression analysis (RA), with lower estimating errors for predicting the HPC slump.