Geopolymer concrete (GPC) based on fly ash (FA) is being studied as a possible alternative solution with a lower environmental impact than Portland cement mixtures. However, the accuracy of the strength prediction still needs to be improved. This study was based on the investigation of various types of machine learning (ML) approaches to predict the compressive strength (C-S) of GPC. The support vector machine (SVM), multilayer perceptron (MLP), and XGBoost (XGB) techniques have been employed to check the difference between the experimental and predicted results of the C-S for the GPC. The coefficient of determination (R2) was used to measure how accurate the results were, which usually ranged from 0 to 1. The results show that the XGB was a more accurate model, indicating an R2 value of 0.98, as opposed to SVM (0.91) and MLP (0.88). The statistical checks and k-fold cross-validation (CV) also confirm the high precision level of the XGB model. The lesser values of the errors for the XGB approach, such as mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE), were noted as 1.49 MPa, 3.16 MPa, and 1.78 MPa, respectively. These lesser values of the errors also indicate the high precision of the XGB model. Moreover, the sensitivity analysis was also conducted to evaluate the parameter’s contribution towards the anticipation of C-S of GPC. The use of ML techniques for the prediction of material properties will not only reduce the effort of experimental work in the laboratory but also minimize the cast and time for the researchers.

This paper analyzed the activity of coal-based metakaolin and non-coal-based metakaolin (ordinary metakaolin) commonly used in the Chinese market. The content of kaolin was detected by XPF. The phases of kaolin, metakaolin and corresponding alkali-excited reactants were detected by XRD. The contents of Al (IV), Al (V) and Al (VI) in kaolin and metakaolin were analyzed by 27Al NMR. The micromorphology of kaolin and metakaolin were observed by SEM. The water-resistance of alkali-activated metakaolin was tested by immersion experiment. The results showed that a lot of corundum and quartz were present in the coal-based metakaolin, which was caused by over calcination. Furthermore, large amounts of sillimanite, quartz and cristobalite existed in ordinary metakaolin with a low content of amorphous aluminum silicate, which was caused by excessive impurities in the raw materials and over calcination. These crystalline substances could not react in an alkali solution, and their existence reduced the activity of the two metakaolins. Both of the two metakaolin production methods need to be improved to increase the activity.

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