A supervised machine learning modeling with parametric optimization of the compressive strength of graphene-nano-engineered concrete

ABSTRACT This study explores the potential of nano-graphene particles for the sustainable manufacturing of concrete. The primary goal is to predict the compressive strength of graphene-incorporated concrete by evaluating the effects of materials such as cement, graphene, fly ash, water usage, aggregate levels, curing ages, and superplasticizer dosages. A total of 350 data entries were sourced from various literature. Multiple machine learning techniques, such as Adaptive Boosting, Decision Tree, Gradient Boosting, k-nearest Neighbors, Light Gradient Boosting, and XGBoost, were utilized to study how these variables influence compressive strength. The dataset was split into an 80%–20% train and test set for developing the prediction algorithms. Among the models, the XGBoost model delivered the highest precision, with a coefficient of determination of 0.921 in the training stage. To ascertain how the input parameters affected the outcome, SHAP and partial dependence plots were employed for each variable's contribution to the improvement of strength. Based on the parametric optimization, incorporating graphene at a range of 0–2 kg/m3 in the concrete mix led to the maximum increase in compressive strength. This work enhances machine learning-based nano-engineered concrete production by removing experimental methods, reducing labor and resource demands, minimizing environmental footprint, and providing graphene-modified concrete design data.