An extended multi-model regression approach for compressive strength prediction and optimization of a concrete mixture

Abstract

Due to the significant delay and cost associated with experimental tests, a model-based evaluation of concrete compressive strength is of high value, both for the purpose of strength prediction as well as the mixture optimization. In contrast to the prior recent studies employing a single regression model, in this paper, we present a combined multi-model framework where the regression methods based on artificial neural network, random forest regression and polynomial regression are jointly implemented for compressive strength prediction with a higher accuracy. The outcomes of the individual regression models are combined via a linear weighting strategy and optimized over the training data set as a quadratic convex optimization problem. It is worth mentioning that due to the convexity of the formulated problem, the globally optimum weighting strategy is obtained via standard numerical solvers. Afterwards, employing the obtained regression model, a multi-objective genetic algorithm-based method is proposed for mixture optimization under practical constraints, where a Pareto front of the cost-CS trade-off has been obtained employing the available data set. Numerical evaluations show that the proposed multi-model regression achieves a significantly higher prediction accuracy, i.e., approximately 18% reduction in the obtained prediction mean squared error, without weight optimization, and roughly 30% reduction in the obtained prediction mean squared error with an optimized combination following a convex quadratic optimization, compared to the best single model regression method employing a multi-layered artificial neural network.