| Popis: |
Contemporary infrastructure requires structural elements with enhanced mechanical strength and durability. Integrating nanomaterials into concrete is a promising solution to improve concrete strength and durability. However, the intricacies of such nanoscale cementitious composites are highly complex. Traditional regression models encounter limitations in capturing these intricate compositions to provide accurate and reliable estimations. This study focuses on developing robust prediction models for the compressive strength (CS) of graphene nanoparticle-reinforced cementitious composites (GrNCC) through machine learning (ML) algorithms. Three ML models, bagging regressor (BR), decision tree (DT), and AdaBoost regressor (AR), were employed to predict CS based on a comprehensive dataset of 172 experimental values. Seven input parameters, including graphite nanoparticle (GrN) diameter, water-to-cement ratio (wc), GrN content (GC), ultrasonication (US), sand content (SC), curing age (CA), and GrN thickness (GT), were considered. The models were trained with 70 % of the data, and the remaining 30 % of the data was used for testing the models. Statistical metrics such as mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R) were employed to assess the predictive accuracy of the models. The DT and AR models demonstrated exceptional accuracy, yielding high correlation coefficients of 0.983 and 0.979 for training, and 0.873 and 0.822 for testing, respectively. Shapley Additive exPlanation (SHAP) analysis highlighted the influential role of curing age and GrN thickness (GT), positively impacting CS, while an increased water-to-cement ratio (w/c) negatively affected CS. This study showcases the efficacy of ML techniques in accurately predicting CS of graphene nanoparticle-modified concrete, offering a swift and cost-effective approach for assessing nanomaterial impact on concrete strength and reducing reliance on time-consuming and expensive experiments. |
