  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
this regard, several recent studies have employed state-of-the-art regression
models in order to achieve a good prediction model, employing available
experimental data sets. Nevertheless, while each of the employed models can
better adapt to a specific nature of the input data, the accuracy of each
individual model is limited due to the sensitivity to the choice of
hyperparameters and the learning strategy. In the present work, we take a
further step towards improving the accuracy of the prediction model via the
weighted combination of multiple regression methods. Moreover, a (GA)-based
multi-objective mixture optimization is proposed, building on the obtained
multi-regression model. In particular, we present a data aided framework where
the regression methods based on artificial neural network, random forest
regression, and polynomial regression are jointly implemented to predict the
compressive strength of concrete. The outcome of the individual regression
models are then 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.
Employing the proposed GA-based optimization, a Pareto front of the cost-CS
trade-of has been obtained employing the available data set. Moreover, the
resulting accuracy of the proposed multi-model prediction method is shown to
outperform the available single-model regression methods in the literature by a
valuable margin, via numerical simulations.
