Optimization design method of C30/C40 fly ash concrete based on machine learning and elite retention genetic algorithm

This paper establishes an intelligent optimization design method for fly ash (FA) concrete considering 28-day compressive strength, slump, and carbon emissions based on machine learning (ML) and elite retention genetic algorithm (EGA). The results demonstrate that the Extreme Gradient Boosting (XGB) model achieves high accuracy in predicting compressive strength, while Gradient Boosting (GB) shows higher accuracy and generalization ability in predicting slump. The water-to-binder ratio and cement content have a significant impact on the compressive strength of FA concrete. Reducing the water-to-binder ratio or increasing cement content helps improve compressive strength. The dosage of superplasticizer and the water content are key factors in controlling the slump. Properly increasing the dosage of superplasticizer and water content can effectively improve the slump of concrete. The FA concrete intelligent design system developed based on the XGB model, GB model, and EGA algorithm can efficiently obtain the optimal preparation parameters and accurately predict the corresponding performance. Furthermore, the carbon emissions of the optimized C30 and C40 FA concrete decrease by 12.72 % and 17.44 % respectively compared to the baseline concrete. Finally, the experimental results verify the prediction accuracy and generalization ability of the XGB and GB models, with the relative prediction error of C30 and C40 FA concrete both being less than 8 %.