Unraveling the Synergistic Effects of Fibers and Recycled Aggregates in Concrete Using Ensemble Machine-Learning Approach

Abstract

Machine learning is often used to predict concrete properties, but its ability to derive fundamental insights for mix design remains underexplored. This study leverages ensemble methods to predict the split tensile strength (STS) of fiber-reinforced recycled aggregate concrete. The primary novelty of this work lies in building on the results of predictive models to decode the complex interactions governing their performance, which then leads to actionable design recommendations by employing model interpretability techniques. A dataset consisting of 257 samples, representing 11 variables, was analyzed using regression tree, boosted regression tree (BRT), and random forest tree models. The BRT model had the lowest error (root mean square error = 0.49 MPa) among these models. Interpretation of the optimal BRT model revealed that the density of recycled aggregate (RCA) had the highest impact on the model followed by the contents of water, cement, RCA, and superplasticizer (SP). These top five parameters collectively contributed to more than 90% of the prediction variance. Critically, the analysis uncovered nonlinear interactions and optimal design thresholds. The best results (STS of up to 7.7 MPa) were obtained with steel fibers at specific mix parameters, which were identified by the model as cement content ( > 400    kg / m 3 ), RCA density > 2,500    kg / m 3 , and SP dosage (4%–5%). The results of this study are expected to promote the use of durable and sustainable materials in construction by moving beyond predictive modeling to provide actionable, interpretable insights for composite mix design of concrete with multiple nontraditional materials used in tandem.