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The construction industry is facing challenges from the hazardous nature of Ordinary Portland Cement (OPC) production as one of the main contributors to global warming and CO2 emission. Given its increasing demand, the need to replace OPC with sustainable alternatives for green concrete production is essential. The promising results of using fly ash (FA) in concrete mixtures as a supplementary cementitious material (SCM) have attracted great attention. However, conventional laboratory procedures for the analysis of concrete properties like Compressive Strength (CS) are laborious and expensive. The objective of this paper is to predict the CS of fly ash concrete using five integrated models (BP-ANN, BP-GA, BP-PSO, RF, and XGBoost) in order to develop an accurate method for predicting the CS of fly ash concrete and to reduce the error of the prediction model, and to quantify the effect of parameter variations on the CS by using it for parameter impact analysis. In addition, performance statistics for the five algorithmic models mentioned above show that the BP-GA model has the best prediction performance for fly ash concrete CS, with prediction errors distributed within ±5%,The sensitivity investigation concludes that the water-to-binder ratio (w/b) has the greatest influence on the CS of fly ash concrete. Through parametric analysis, it was found that the early CS of fly ash concrete increased with increasing fly ash content when the fly ash replacement rate (FA%) was less than 30%, and decreased with increasing fly ash content when the FA% was greater than 30%.
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