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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. [Display omitted] • Proposing a data-aided regression method for concrete compressive strength prediction. • Employing a combined multi-model approach to improve prediction accuracy. • Mixture parameter optimization via NSGA-II, employing the obtained regression model. [ABSTRACT FROM AUTHOR]

• A deep neural network model (DNN) is developed to predict the compressive strength of rubber concrete. • The database construction procedure focuses on the choice of different input parameters in the input space. • The compressive strength specimen type is considered as input parameter. • The accuracy of the proposed DNN model reaches an outstanding value. • Alternative possibilities for the input space are discussed from a practical engineering point of view. This paper presents an innovative development process of a Deep Neural Network model to predict the compressive strength of rubber concrete. To this goal, a rubber concrete database is carefully constructed, incorporating a set of binder, aggregate, and other related concrete variables as input parameters, whereas the compressive strength is considered as output. The development of the DNN model includes extensive analysis of the number of hidden layers and the neurons in each layer, combining with a statistical investigation of the models' prediction outputs. The results show that the DNN model outperforms other neural network structures according to several well-known performance indices, such as coefficient of determination, root mean square error, and mean absolute error. The proposed DNN model also exhibits higher prediction accuracy than previously published results, using different machine learning algorithms in the literature. A sensitivity analysis using partial dependence plots is performed within the DNN algorithm in order to achieve an in-depth examination of the influence of each single input variable on the predicted compressive strength of rubber concrete. Finally, the possibility of using other input variables is given to pave the way for applications in regular, high strength, or light-weight foamed concrete containing rubber particles. [ABSTRACT FROM AUTHOR]