Modeling and analysis of concrete slump using artificial neural networks

Artificial neural network (ANN) and regression models are developed for the estimation of concrete slump using concrete constituent data. The concrete mix constituent and slump data from laboratory tests have been employed to develop all models. The results obtained in this study demonstrate the superiority of the ANN models. It was found that combining one or more concrete mix constituents and treating them as an independent input variable is not advantageous when using regression but can be very useful when using ANNs for modeling concrete slump. Sensitivity analyses based on the ANN models were carried out to evaluate the impact of different concrete mix constituents on the slump values. It was found that the slump attains a minimum value at the critical levels of mortar and coarse aggregates, and tends to increase with paste content and decrease with sand content in the concrete mix. DOI: 10.1061/(ASCE)0899-1561(2008)20:9(628) CE Database subject headings: Artificial networks; Neural networks; Concrete; Aggregates; Estimation; Sensitivity analysis; Regression analysis.