## 5 Conclusions  
In this study, predictive models were developed for concrete compressive strength, flexural strength, and slump values using various supervised learning techniques. The models were developed following the TDSP methodology. The following conclusions are drawn:  
* Various supervised learning models were trained and tested to estimate compressive and flexural strength, as well as slump, revealing promising performance. Among these models, the MLP demonstrated superior efficacy in a 10-fold-cross-validation. * The MLP model achieved an MAE of 4.36 MPa and an MAPE of 11.41% for compressive strength estimation, indicating its robust predictive power. The results are in line with the established objectives of an MAE below 10 MPa. * The MLP model for flexural strength prediction achieved an MAE of 0.7 MPa and an MAPE of 14.05%. These results align with the established goal of achieving an MAE less than 1.2 MPa. * The MLP model for slump prediction achieved an MAE of 11.7 mm and an MAPE of 7.27%. These findings are consistent with the target of achieving an MAE under 20 mm. * Our approach differed from conventional methods by utilizing GA, BO, and PSO for hyperparameter tuning, with GA proving to be the most effective. * Comparisons with other studies underscored our models' competitive performance despite the differences in dataset size and attributes. While our models achieved comparable RMSE values, slight disparities in \(R^{2}\) values were noted, likely attributed to dataset variations. Nonetheless, our predictive models for compressive strength, flexural strength, and slump exhibited promising accuracy and reliability. * Regarding the datasets constructed for this study, it is important to highlight that in the observed studies, no comparable datasets were found with ours. Unlike our case, it is common to find that studies conclude that they need larger datasets. * Finally, our study stands out from other research endeavors by executing the deployment process within a real-world environment. In the deployment phase, a meticulous process was executed to implement the predictive models in an operational environment. Conceptualization, J.F.V. and A.I.O. ; data curation, J.F.V., A.I.O., N.A.O., and E.O. ; formal analysis, J.F.V., A.I.O., and J.M.L. ; funding acquisition, J.F.V. and A.I.O. ; investigation, J.F.V., A.I.O., N.A.O., E.O., A.G., and J.M.L. ; methodology, J.F.V., A.I.O., and E.O. ; project administration, J.F.V. and A.I.O. ; resources, J.F.V. and A.I.O. ; software, J.F.V., A.I.O., N.A.O., and E.O. ; supervision, J.F.V. and A.I.O. ; validation, J.F.V., A.I.O., A.G., and J.M.L. ; visualization, J.F.V. and A.I.O; writing--original draft, J.F.V., A.I.O., N.A.O., and E.O. ; writing--review and editing, J.F.V., A.I.O., A.G., and J.M.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Minicencias Colombia, grant number 193-2021. Not applicable. The raw data supporting the conclusions of this article are proprietary industry data. However, a portion of the compressive strength dataset will be made available by the authors on request. Author [PERSON] was employed by the company Cementos Argos. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The following abbreviations are used in this manuscript: