## 4 Discussion
In the study outlined in [27], where 1030 sets of concrete compressive strength were analyzed using 5-fold-cross-validation, a \(R^{2}=0.9\) and a RMSE = 4.8 MPa were obtained. Our predictive model, trained with a significantly larger dataset, achieved similar performance in RMSE but lower in \(R^{2}\). However, we consider that the results of the other research are not comparable to ours due to the number of input variables and samples used to train and evaluate the models. Additionally, in the other research, the age of the concrete is an input instance, a variable that may have small values, favoring the \(R^{2}\) measurement. Theoretically, higher concrete age is expected to result in lower predictive capability. Our predictive model for 28-day flexural strength, trained with 514 data instances, yielded an \(R^{2}=0.69\), RMSE = 0.57 MPa, and MAE = 0.7 MPa in 10-fold-cross-validation. Conversely, the study outlined in [31] reported an \(R^{2}\) = 0.96, RMSE = 1.8 MPa, and MAE = 1.3 MPa in a 10-fold-cross-validation in the prediction of 28-days flexural strength using 173 data points. In the other research, a higher \(R^{2}\) was achieved but with poorer RMSE and MAE values using a smaller dataset. Finally, the predictive slump model, trained with 19,536 data instances, achieved \(R^{2}=0.84\) and RMSE = 11.3 mm during 10-fold-cross-validation. In the study outlined in [35], modeling with a neural network on 103 data instances using a 70-30% train-test split yielded an \(R^{2}\) of 0.95 and an RMSE of 2.781 cm (equivalent to 27.81 mm). We consider that the results are not directly comparable due to significant differences in the quantity of data. Nonetheless, our model achieved a lower RMSE compared to the neural network model in the cited study. The models were deployed on a web platform, then were monitored on an independent dataset. In the monitoring of the final models, prediction vs. actual value graphs were created (Figures 7-9). The proximity of the points to the diagonal line in these graphs indicates the accuracy and reliability of the predictions. Overall, a consistent distribution along the diagonal was observed in the compressive and flexural strength graphs, suggesting reliable predictions in these aspects. However, in the slump graph, although most predictions aligned with the diagonal, considerable deviations were identified in a small number of cases. The \(R^{2}\) and RMSE metrics were also calculated during the monitoring phase. In this phase the predictive model for compressive strength (Figure 7) obtained an \(R^{2}\) of 0.77 and an RMSE of 5.0 MPa. This suggests that the model explains approximately 77% of the variability in the compressive strength data, which is acceptable given the low values of MAE = 4.36 MPa and MAPE = 11.41% in the cross-validation. During the monitoring of the flexural model (Figure 8), we obtained an \(R^{2}\) of 0.73 and an RMSE of 0.55 MPa, which indicates that approximately 73% of the variability in the dependent variable can be explained by the prediction model. The low values of MAE = 0.7 MPa and MAPE = 14.05% in the cross-validation enhance the reliability of the model. Finally, during the slump model monitoring (Figure 9), we obtained an \(R^{2}\) of 0.91 and RMSE of 9.9 MPa. This suggests that the model explains approximately 91% of the variability in slump data. The model's quality is also verified by the MAE = 11.7 mm and MAPE = 7.27% obtained in the cross-validation. Given the strong performance of the slump prediction model, demonstrated by the high values of \(R^{2}\) and low values of MAE and MAPE, the presence of 10 deviations in the predictions (Figure 9) could be attributed to either uncommon values in the input variables or atypical slump measurements, possibly influenced by factors not considered in the model, such as meteorological conditions. For future research directions, several areas can be considered to expand and improve the scope and application of the developed predictive models.