The Application of Response Surface Methodology and Machine Learning for Predicting the Compressive Strength of Recycled Aggregate Concrete Containing Polypropylene Fibers and Supplementary Cementitious Materials

Mohammed K. Alkharisi and Hany A. Dahish ()
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Mohammed K. Alkharisi: Department of Civil Engineering, College of Engineering, Qassim University, Buraidah 52571, Saudi Arabia
Hany A. Dahish: Department of Civil Engineering, College of Engineering, Qassim University, Buraidah 52571, Saudi Arabia

Sustainability, 2025, vol. 17, issue 7, 1-28

Abstract: The construction industry’s development trend has resulted in a large volume of demolished concrete. Improving the efficiency of the proper use of this waste as a recycled aggregate (RA) in concrete is a promising solution. In this study, we utilized response surface methodology (RSM) and three machine learning (ML) techniques—the M5P algorithm, the random forest (RF) algorithm, and extreme gradient boosting (XGB)—to optimize and predict the compressive strength (CS) of RA concrete containing fly ash (FA), silica fume (SF), and polypropylene fiber (PPF). To build the models, the results regarding 529 data points were used as a dataset with varying numbers of input parameters (out of a total of ten). The CS quadratic model under RSM exhibited acceptable prediction accuracy. The best CS was found with a 100% volume of RA consisting of coarse aggregate, 1.13% PPF by volume of concrete, 7.90% FA, and 5.30% SF as partial replacements of binders by weight. The XGB model exhibited superior performance and high prediction accuracy, with a higher R² and lower values of errors, as depicted by MAE, RMSE, and MAPE, when compared to the other developed models. Furthermore, SHAP analysis showed that PPF had a positive impact on predicting CS, but the curing age and superplasticizer dose had the highest positive impact on predicting the CS of RA concrete.

Keywords: recycled aggregate; polypropylene fiber; supplementary cementitious materials; response surface methodology; optimization; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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