Research Article

The determination of the concrete compressive strength remains a challenging task in the concrete industry. Machine learning (ML) algorithms offer an alternative and this study presents a comparative analysis of five ML regression models; Gradient Boosting (GB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), and Linear Regression (LR) on a dataset of 1030 concrete samples. The findings indicate that the GB model achieved the best performance. The developed GB model achieved R-squared values of 91.60%, 91.43%, and 90.18% for the 10-fold, 5-fold, and 3-fold cross-validations, respectively, with mean absolute error, root mean squared error, and mean absolute percentage error values of 2.6776, 4.3523, and 9.19%, respectively. The GB model trained and evaluated was deployed to a web application using Streamlit for real-time prediction of the concrete compressive strength. The results of this research offer a precise and practical method for judging the quality of concrete constructions.

Machine learning Concrete compressive strength Prediction Regression models Web application

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Year 2024,
Volume: 7 Issue: 2, 127 - 137, 31.08.2024
### Abstract

### References

- ACI, AMERICAN CONCRETE INSTITUTE. ACI 318: Building Code Requirements for Structural Concrete. American Concrete Institute, 2008.
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- J. P. Zaniewski, MATERIALS FOR CIVIL AND 3 rd Edition Michael S . Mamlouk Arizona State University. 2011.
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- U. Singh, M. Rizwan, M. Alaraj, and I. Alsaidan, “A machine learning-based gradient boosting regression approach for wind power production forecasting: A step towards smart grid environments,” Energies, vol. 14, no. 16, pp. 1–21, 2021, doi: 10.3390/en14165196.
- L. E. de Oliveira Aparecido, G. de Souza Rolim, J. R. da Silva Cabral De Moraes, C. T. S. Costa, and P. S. de Souza, “Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases,” Int. J. Biometeorol., vol. 64, no. 4, pp. 671–688, 2020, doi: 10.1007/s00484-019-01856-1.
- C. Araújo, C. Soares, I. Pereira, D. Coelho, M. Â. Rebelo, and A. Madureira, “A Novel Approach for Send Time Prediction on Email Marketing,” Appl. Sci., vol. 12, no. 8310, pp. 1–13, 2022, doi: 10.3390/app12168310.
- A. O. Oyedeji, A. M. Salami, O. Folorunsho, and O. R. Abolade, “Analysis and Prediction of Student Academic Performance Using Machine Learning,” JITCE (Journal Inf. Technol. Comput. Eng., vol. 4, no. 01, pp. 10–15, 2020, doi: 10.25077/jitce.4.01.10-15.2020.

There are 32 citations in total.

Primary Language | English |
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Subjects | Software Engineering (Other) |

Journal Section | Articles |

Authors | |

Early Pub Date | August 23, 2024 |

Publication Date | August 31, 2024 |

Submission Date | January 18, 2024 |

Acceptance Date | July 10, 2024 |

Published in Issue | Year 2024Volume: 7 Issue: 2 |