Data driven tensile strength prediction for fiber-reinforced rubberized recycled aggregate concrete using machine learning
The structural integrity and long-term durability of concrete depend on its tensile strength, which endows the material with the capacity to resist crack initiation and propagation. The tensile strength of concrete is largely influenced by the mixing proportions, the type of aggregates, and the pres...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Cleaner Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772397625000322 |
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| Summary: | The structural integrity and long-term durability of concrete depend on its tensile strength, which endows the material with the capacity to resist crack initiation and propagation. The tensile strength of concrete is largely influenced by the mixing proportions, the type of aggregates, and the presence of fibers or additives. The incorporation of different ingredients and mixing proportions makes this property nearly unpredictable. To tackle this, this research examined the tensile strength behavior of fiber-reinforced rubberized recycled aggregate concrete (FR3C) using nine machine learning (ML) models. In this study, nine machine learning models—Random Forest, K-Nearest Neighbors, Support Vector Regression, Decision Tree, Artificial Neural Network, AdaBoost, Gradient Boost, CatBoost, and Extreme Gradient Boost—were trained and tested using a dataset of 346 samples representing various mix proportions. The models were applied to predict the tensile strengths of the concrete and to determine the optimal proportions of ingredients. Key input characteristics include water-to-cement ratio (W/C), nominal aggregate size, rubber content, amount of recycled coarse aggregate (RCA), type of fiber and usage, plasticizer use, fly ash (%), and compressive strength. The findings showed that K-Nearest Neighbors performed best in predicting FR3C tensile strength, achieving the lowest mean absolute error MAE (0.001) and root mean squared error (RMSE 0.001) and highest coefficient of determination (R2 = 0.999) in test scores. The Shapley Additive Explanations (SHAP) analysis indicated that compressive strength, W/C ratio, and fiber (%) are the most influential parameters affecting the tensile strength of FR3C. Moreover, increased W/C ratios and higher plasticizer content were associated with a 60–72 % reduction in tensile strength. This research may contribute to practical concrete mix design in the construction industry and also in the design process of structural elements particularly for crack width control and mitigation. Therefore, it is feasible to increase the usage of FR3C concrete by precisely forecasting its tensile strength, transforming wastes into resources, and minimizing the adverse environmental effects of construction materials. |
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| ISSN: | 2772-3976 |