Machine learning approach for predicting the compressive and flexural strengths of fiber-reinforced concrete mixtures
Roz-Ud-Din NASSAR, Khaled ABDALGADER
Abstract. This study employed the Extreme Gradient Boosting (XGBoost) Machine Learning (ML) model to predict the compressive and flexural strengths of concrete mixtures reinforced with 0.5, 1.0, and 1.5% of polyvinyl alcohol (PVA) fibers volume fractions at 28 days of concrete age. A significant increase in the compressive and flexural strengths of the resulting concrete mixtures was recorded as a result of the incorporation of PVA fibers, particularly at the higher fiber volume fraction level when compared with that of control mixture produced without the addition of PVA fibers. The ML model-predicted strengths data was found to be in close conformity with the experimentally tested one, pointing at the high accuracy of XGBoost model. Statistical analysis of the output variable data yielded lower error and higher R2 values (0.931 for compressive and 0.922 for flexural strengths) which confirm the suitability of the XGBoost model to predict the two types of strength characteristics of the PVC fiber reinforced concrete mixtures with high accuracy. The use of ML-model to predict the strength characteristics of PVA fiber-reinforced concrete mixtures is viewed to be a time-efficient method having high level of accuracy.
Keywords
Concrete, Machine Learning Model, XGBoost, Compressive Strength, Flexural Strength
Published online 2/25/2025, 10 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Roz-Ud-Din NASSAR, Khaled ABDALGADER, Machine learning approach for predicting the compressive and flexural strengths of fiber-reinforced concrete mixtures, Materials Research Proceedings, Vol. 48, pp 407-416, 2025
DOI: https://doi.org/10.21741/9781644903414-45
The article was published as article 45 of the book Civil and Environmental Engineering for Resilient, Smart and Sustainable Solutions
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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