Predicting the rheology of self-consolidating concrete under hot weather

Abstract

The flow behaviour of self-consolidating concrete (SCC) incorporating several types of supplementary materials was investigated under hot weather conditions (25–40°C) and prolonged mixing (up to 110 min). Experiments were conducted outdoors during the summer of 2014 in Abu Dhabi. The slump flow and rheological properties of SCC incorporating various types of supplementary cementitious materials (SCMs) were examined under such types of harsh environmental conditions. A portable concrete rheometer (BT2) was used to measure the rheological properties of the investigated SCC mixtures. In this study, the neural network technique was employed to predict the rheological properties of SCC under hot weather conditions and prolonged mixing. The ambient temperature, mixing time and SCMs were the network input parameters. The relative viscosity, relative yield stress and slump flow were the output parameters. The optimum network architecture was selected based on Akaike information criterion and mean absolute percentage error. Furthermore, a parametric study was conducted to investigate the sensitivity of the input variables to the developed artificial neural network (ANN) models using the Garson equation. The results showed that the developed ANN models were capable of predicting the rheological properties and slump flow of SCC as a function of temperature and mixing time.