Abstract

Experimental determination of concrete compressive strength is time-consuming and costly. This study predicts the 28-day compressive strength of high-performance concrete from mixture components and curing age using ensemble learning. XGBoost, random forest, gradient boosting and artificial neural networks were compared on a dataset of 1,030 experiments. XGBoost showed the highest performance with a coefficient of determination of 0.94. SHAP analysis confirmed the dominant influence of the water-to-cement ratio and curing age on strength.

Keywords:concretecompressive strengthensemble learningXGBoostcivil engineering

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Declarations

Ethics Approval
This study does not require ethics committee approval.
Conflict of Interest
The authors declare no conflict of interest.
Funding
This work was supported by the Selçuk University Scientific Research Projects Coordination Unit (Project No: 24012).

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How to Cite

Erdem, H., & Çelik, D. (2026). Predicting the Compressive Strength of High-Performance Concrete with Ensemble Learning. International Journal of Science and Technology Research, 8(1), 1–14. https://doi.org/10.99999/ubtad.2026.13
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CC BY 4.0

© 2026 Hasan Erdem, Deniz Çelik. This article is distributed under the terms of the CC BY 4.0 license, which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. License text