Self-consolidating concrete (SCC) has recently drawn attention to the construction industry in hot weather countries, due to its high fresh and mechanical properties. The slump flow is routinely used for quality control of SCC. Experiments were conducted by the current authors to investigate the effects of hot weather conditions on the slump flow of SCC. Self-consolidating concrete mixtures were prepared with different dosages of fly ash and superplasticizer and under different ambient temperatures. The results showed that the slump flow of SCC is sensitive to changes in ambient temperature, fly ash dosage, and superplasticizer dosage. In this paper, several artificial neural networks (ANNs) were employed to predict the slump flow of self-consolidating concrete under hot weather. Some of the data used to construct the ANNs models in this paper were collected from the experimental study conducted by the current authors, and other data were gathered from literature. Various parameters including ambient temperature and mixing time were used as inputs during the construction of ANN models. The developed ANN models employed two neural networks: the Feed-Forward Back Propagation (FFBP) and the Cascade Forward Back Propagation (CFBP). Both FFBP and CFBP showed good predictability to the slump flow of SCC mixtures. However, the FFBP network showed a slight better performance than CFBP, where it better predicted the slump flow of SCC than the CFBP network under hot weather. The results in this paper indicate that the ANNs can be employed to help the concrete industry in hot weather to predict the quality of fresh self-consolidating concrete mixes without the need to go through long trial and error testing program.Keywords: Self-consolidating concrete; Neural networks; Hot weather, Feed-forward back-propagation, Cascade-forward back propagation.
Influence of mixing time and superplasticizer dosage on self-consolidating concrete properties