Prediction of Compressive Strength of Fly Ash-Based Geopolymer Concrete Using Supervised Machine Learning Methods

The use of fly ash (FA)-based geopolymer concrete as a low-carbon and eco-friendly substitute to Portland cement concrete has gained attention in recent years. However, accurately predicting its compressive strength remains a challenge due to the complex chemical and physical interactions involved in the geopolymerization process. In this research, three machine learning models, namely backpropagation neural network (BPNN), random forest regression (RFR), and k-nearest neighbors (KNN), were employed to predict the compressive strength of FA-based geopolymer concrete. The models were trained, validated, and tested using a dataset that considered the chemical composition, mix proportions, and pre-curing conditions of the concrete. The performance of each model was assessed utilizing various metrics, including the coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). The results indicated that the BPNN model gave the best results with an R2 value of 0.948 relative to RFR and KNN with R2 values of 0.927 and 0.911, respectively. The permutation feature importance (PFI) index revealed that the coarse aggregate content, SiO2 content in FA, and NaOH concentration were found to have the greatest impact on the compressive strength of the FA-based geopolymer concrete.