Application of novel deep neural network on prediction of compressive strength of fly ash based concrete

Fly ash (FA)-based high-strength concrete (HSC) has attracted significant interest due to its potential to substitute Portland cement, offering both environmental benefits and improved performance. However, the design of FA-HSC is challenging, as key factors such as fly ash percentage, water content, and superplasticizer dosage have a complex influence on compressive strength. This study aims to develop an efficient predictive tool for FA-HSC mix design, using artificial intelligence (AI) models to address the inherent variability and uncertainty in these parameters. Six AI models, including a Deep Neural Network (DNN), were employed to analyse the relationships between mix design variables and compressive strength. The DNN model, in particular, demonstrated superior performance compared to the other models, with a high coefficient of determination (R2 = 0.89), variance accounted for (VAF = 88.3%), root mean square error (RMSE = 0.06), and residual standard error (RSR = 0.31). These results indicate that the DNN model can provide reliable predictions of compressive strength, offering a more efficient alternative to traditional trial-and-error methods. The AI-based approach can save both time and material costs while optimising performance. Overall, this AI-driven model contributes to the advancement of sustainable concrete technology by enabling more precise and resource-efficient mix designs for FA-based high-strength concrete.