A success history-based adaptive differential evolution optimized support vector regression for estimating plastic viscosity of fresh concrete

Plastic viscosity is an important parameter of fresh concrete mixes. This research investigates a machine learning-based method for constructing a functional mapping between concrete mix properties and the plastic viscosity. The investigated machine learning method relies on the support vector regression (SVR) which is a robust method for nonlinear and multivariate function approximation. Moreover, the history-based adaptive differential evolution with linear population size reduction (L-SHADE) is employed to optimize the SVR model construction phase. Thus, the proposed method, named L-SHADE-SVR, is an integration of machine learning and metaheuristic optimization. To train and verify the L-SHADE-SVR model, a dataset consisting of 142 experimental tests was collected. Experimental results with repetitive phases of model training and testing reveal that the newly constructed model is capable of delivering highly accurate estimation of the plastic viscosity with mean absolute percentage error of 12% and coefficient of determination of 0.82. These outcomes are superior compared to the employed benchmark methods including artificial neural network, multivariate adaptive regression spline, and sequential piecewise multiple linear regression. Therefore, the L-SHADE-SVR model is a promising tool to assist construction engineers in estimating the plastic viscosity of fresh concrete mixes.