article · Scientific Reports
Cement-based building materials often suffer from brittleness, prompting interest in nanomaterials such as graphene to strengthen their structural matrices. Developing optimal mix designs can be challenging, leading to the creation of a machine learning framework that predicts the compressive strength of graphene-modified concrete. Trained on a curated database of 371 mix formulations, computational models evaluated seven mix variables alongside curing age. A hybrid system combining Extreme Gradient Boosting and Bagging delivered the strongest predictive accuracy, outperforming other ensemble techniques. Interpretability analyses showed that water content and curing duration were the primary drivers of concrete strength, whereas graphene introduced complex, non-linear effects. Raising graphene additions between zero and two kilograms per cubic metre boosted predicted compressive strength from roughly 53 to 60 megapascals. This data-driven strategy offers a practical means to guide mix optimisation for stronger, more efficient composite concretes.
Graphene has the potential to make concrete stronger and more durable, but identifying the correct recipe through physical trial and error is slow and expensive. Using machine learning to forecast material performance from mix recipes accelerates the formulation of resilient construction materials, helping engineers design higher-performing structures more efficiently and sustainably.
The predictive tool could assist concrete manufacturers and structural materials engineers in optimising graphene-infused mix proportions without conducting exhaustive physical batch testing. Because the model relies on historical data collated from 371 published trials, it sits at an applied research stage. Transition to commercial software or deployment in ready-mix plants would require direct testing against specific industrial production setups and real supply-chain aggregate variations.
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The quasi-brittle response of cementitious materials continues to limit their structural efficiency, necessitating advanced reinforcement strategies. While conventional fibers improve mechanical performance, the emergence of graphene-based nanomaterials offers new opportunities to tailor interfacial interactions and enhance matrix cohesion at multiple scales. This study presents a data-driven framework for predicting the compressive strength of graphene-modified concrete by integrating mix design parameters and machine learning techniques. A curated database of 371 mix compositions was assembled from published studies, incorporating seven mix variables along with the curing period. Three ensemble learning models, Extreme Gradient Boosting, Adaptive Boosting, and Bagging Regressor, together with two hybrid configurations, were developed and evaluated. Model performance was assessed using standard statistical indices, with particular emphasis on generalization across unseen data. Based on the outcomes of this study, the hybrid Extreme Gradient Boosting–Bagging model demonstrated the highest predictive capability, achieving R 2 values of 0.92 and 0.87 for training and testing datasets, respectively, outperforming individual and alternative ensemble models. Interpretability analysis using SHapley Additive Explanations identified water (+ 14.04) and curing age (+ 6.46) content as dominant contributors to strength development, while the influence of graphene and supplementary materials exhibited nonlinear and interaction-dependent effects. Furthermore, PDP analysis summarized that an increase in graphene content within the range of 0–2 kg/m 3 led to a rise in predicted strength from approximately 53 MPa to around 60 MPa. The proposed approach provides both high predictive accuracy and enhanced interpretability, offering a reliable tool for mix design optimization of graphene-enhanced concrete. More broadly, the study underscores the potential of hybrid machine learning frameworks to accelerate the development of next-generation cementitious composites with improved performance and sustainability.
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DOI: 10.1038/s41598-026-66152-6
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