TY  - EJOU
AU  - Kewalramani, Manish 
AU  - Ghodhbani, Refka 
AU  - Mahmoodzadeh, Arsalan 
AU  - Alghamdi, Abdulaziz 
AU  - Karim, Faten Khalid 
AU  - Alanazi, Abed 
AU  - Alqahtani, Abdullah 
AU  - Alsubai, Shtwai 
AU  - Ltifi, Mounir 

TI  - Predicting the Compressive Strength of Sustainable Concrete Containing Recycled Aluminum Beverage Cans Crumb Using Machine Learning Techniques
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 147
IS  - 3
SN  - 1526-1506

AB  - Concrete manufacturing consumes vast quantities of natural resources and contributes significantly to environmental degradation and carbon emissions. Therefore, integrating recycled waste substances into concrete has become a crucial approach to fostering eco-friendly building practices and supporting circular economy concepts. This study investigates the potential of incorporating recycled aluminum beverage can crumbs (RABCC) as a partial replacement for natural coarse aggregates (NCA) in concrete mixtures, focusing on its impact on compressive strength (CS) and the feasibility of its application in structural concrete. A comprehensive experimental program was conducted to assess the mechanical properties of concrete with varying levels of RABCC (up to 30%) and its interaction with other mix parameters, including water-to-cement ratio (w/c), superplasticizer content (SPC), silica fume content (SFC), and curing time (CT), fine aggregate content (FAC), coarse aggregate content (CAC), fly-ash content (FA–C). The experimental findings revealed that low levels of RABCC incorporation (≤5%) led to minimal reductions in CS, with strengths comparable to those of the reference mix. However, at higher replacement levels, significant reductions in CS were observed, with the CS decreasing by up to 29.8% at 20% RABCC. To predict CS across different RABCC contents, a series of machine learning models, including kernel-based methods, ensemble tree models, gradient boosting techniques, and neural networks, were developed and validated using both hold-out and 5-fold cross-validation. The Gaussian process regression (GPR) model demonstrated the best performance, achieving an R<sup>2</sup> of 0.88 and an root mean squared error (RMSE) of 3.33 MPa in hold-out testing, and an R<sup>2</sup> of 0.89–0.94 and an RMSE of 2.37–3.10 MPa in 5-fold cross-validation, confirming its robustness in predicting CS. Additionally, SHapley Additive exPlanations (SHAP) analysis identified the w/c ratio and RABCC content (RABCC–C) as the most influential factors on CS, with RABCC–C exhibiting a moderately negative correlation. This study demonstrated the potential of RABCC as an eco-friendly, sustainable alternative to conventional aggregates in concrete, offering a viable pathway to reduce aluminum waste without significantly compromising the material’s mechanical performance. The results also underscored the importance of optimizing RABCC content to balance sustainability goals with structural performance.
KW  - Recycled aluminum beverage cans crumb; concrete; compressive strength; sustainability; machine learning

DO  - 10.32604/cmes.2026.084696