Effect of composition and curing on alkali activated fly ash-slag binders: Machine learning prediction with a random forest-genetic algorithm hybrid model

The final setting time (FST) and uniaxial compressive strength (UCS) are critical parameters for designing the mixture proportions of alkali-activated materials (AAMs). To understand the influence of the mixture compo-sition on FST and UCS of AAMs, two datasets containing 616 samples for UCS and 278 samples for FST were compiled from published literature. A random forest (RF) model was developed on these datasets to predict FST and UCS of AAMs. The hyperparameters of the RF model were optimized using the Genetic Algorithm (GA). Results show that the hybrid GA-RF model achieved the highest prediction accuracy on the test set of UCS (0.932) and FST (0.997), compared to other machine learning models. The developed model was then used to interpret the influence of mixture composition on FST and UCS. The curing time and water content significantly influenced the UCS, while Na/Al and water contents were more important to FST. The microstructure devel-opment of the AAMs was affected by Ca/Si, Na/Al and Si/Al ratios. To achieve better UCS, the recommended Ca/ Si varied from 1 to 2; Na/Al was slightly lower than 1 and Si/Al ratios changed between 2.5 and 3.5. This study can facilitate the mixture optimization for FA-slag based AAMs.