Prediction of fresh and hardened properties of self-compacting concrete using support vector regression approach

This article presents the feasibility of using support vector regression (SVR) technique to determine the fresh and hardened properties of self-compacting concrete. Two different kernel functions, namely exponential radial basis function (ERBF) and radial basis function (RBF), were used to develop the SVR model. An experimental database of 115 data samples was collected from different literatures to develop the SVR model. The data used in SVR model have been organized in the form of six input parameters that covers dosage of binder content, fly ash, water–powder ratio, fine aggregate, coarse aggregate and superplasticiser. The above-mentioned ingredients have been taken as input variables, whereas slump flow value, L-box ratio, V-funnel time and compressive strength have been considered as output variables. The obtained results indicate that the SVR–ERBF model outperforms SVR–RBF model for learning and predicting the experimental data with the highest value of the coefficient of correlation (R) equal to 0.965, 0.954, 0.979 and 0.9773 for slump flow, L-box ratio, V-funnel and compressive strength, respectively, with small values of statistical errors. Also, the efficiency of SVR model is compared to artificial neural network (ANN) and multivariable regression analysis (MVR). In addition, a sensitivity analysis was also carried out to determine the effects of various input parameters on output. This study indicates that SVR–ERBF model can be used as an alternative approach in predicting the properties of self-compacting concrete.