Prediction of compressive strength of concrete incorporated with jujube seed as partial replacement of coarse aggregate: a feasibility of Hammerstein–Wiener model versus support vector machine

The need for evaluation of compressive strength of a concrete is of utmost importance in civil and structural engineering as one of the factors that determine quality of concrete. In this paper, two artificial intelligence (AI) techniques, namely Hammerstein–Wiener model (HWM) and support vector machine (SVM) were used in the prediction of compressive strength ( σ ). The input variables including curing age ( T ), amount of coarse aggregate (cA), percentage replacement of aggregate (cAR), amount of Jujube seed ( S ) and slump ( D ) as the independent variables. Two evaluation metrics were used to determine the fitness between the computed and the predicted values of the σ namely, Correlation co-efficient ( R ) and determination co-efficient ( R 2 ), while two other metrics were employed to check the errors depicted by each model combination inform of mean square error (MSE) and root mean square error (RMSE). The result obtained from AI-based models revealed that both HWM and SVM showed higher prediction skills in prediction of σ. Overall, the comparative performance results proved that HWM-M4 indicated an outstanding performance of 0.9953 and 0.9982 in both the training and testing stages, respectively.