## 4 Discussion  
Unlike other studies, the present study predicts, in addition to the traditional compressive strength of concrete, flexural strength and slump values. For the estimation of compressive and flexural strength of concrete at 28 days, as well as slump, several supervised learning models (LR, RT, KNN, MLP, SVR, RF, GBoost, and XGBoost) were trained and their performance tested. Using cross-validation, MAPE was measured to assess the predictive abilities of each model. The results from the training phase indicated that the supervised learning models developed in this study performed well in predicting concrete performance variables. Among these models, the MLP model proved to be the most effective compared to the others. For compressive strength estimation, a MLP with a hidden layer of 134 units achieved a MAE of 4.3 MPa and MAPE of 11.4%. For flexural strength estimation, a MLP with a hidden layer of 178 units achieved a MAE of 0.7 MPa and MAPE of 14%. For slump estimation, an MLP with a hidden layer of 193 units achieved an MAE of 11.7 mm and MAPE of 7%. Since the best method for predicting compressive strength was MLP and the best optimization technique was GA, MLP with GA optimization was selected to build the prediction model for flexural strength and slump. The performance of the models obtained  
Figure 9: Actual vs. Predicted Slump Applied on Test Data. demonstrated that this solution is valid, and models with acceptable performance can be obtained in less time than traditional model selection methods. Instead of using the traditional grid search for hyperparameter tuning, we used different methods (GA, BO, PSO). The method that proved to optimize the hyperparameters most effectively was a GA. The data acquisition and understanding phase has been crucial for the success of our study. We have leveraged an extensive dataset collected over a period of more than five years to train and validate our predictive models. The availability of detailed data on compressive strength, flexural strength, and slump has allowed us to develop robust and reliable models. Data cleaning and preparation have been critical aspects of our modeling process. The identification and removal of outliers and null values have ensured the integrity and quality of our datasets. Furthermore, the application of quality rules defined by experts in concrete production has improved the relevance and accuracy of our models. In the modeling phase, feature engineering operations have allowed us to understand the influence of various independent variables on the performance properties of concrete. Using the mutual_info_regression method, we have identified linear and non-linear relationships between the independent variables and the target variables, including compressive strength, flexural strength, and slump of concrete. Our findings confirm that in addition to cement, other components such as retarding and superplasticizing admixtures, supplementary cementitious materials, and water also significantly influence the compressive and flexural strength of concrete. Furthermore, we have observed that the concrete production plant and the source of aggregates can also significantly affect these properties. In order to compare our results with other articles, the \(R^{2}\) and root mean square error (RMSE) measures were calculated. \(R^{2}\) is a statistical measure quantifying the model's ability to explain the variance in the target variable, representing its goodness-of-fit relative to the total variance in the data. The RMSE is a statistical measure representing the square root of the average of the squared errors between a model's predictions and the actual values. A \(R^{2}\) close to 1 indicates a good fit of the model to the data, while a low RMSE indicates good accuracy in the model's predictions. Our predictive model for 28-days-compressive strength, trained with 19,294 data instances, achieved \(R^{2}=0.79\) and RMSE = 5.1 MPa in a 10-fold-cross-validation.