Machine learning to predict workability and compressive strength of low- and high-calcium fly ash–based geopolymers

The potential substitution of Portland cement–based concrete with low- and high-calcium fly ash–based geopolymers was investigated. However, predicting the workability and compressive strength of geopolymers with the desired physical and mechanical properties is a complicated process because of the variety of chemical compositions found in aluminosilicate sources. Therefore, machine-learning techniques were used to predict the physical and mechanical properties of the geopolymers and eliminate the usual trial-and-error laboratory procedures. The experimental and predicted results of geopolymer properties using the multilayer perceptron regressor, voting regressor, and XGBoost techniques were compared. The XGBoost model outperformed the other models in terms of accuracy for predicting workability and compressive strength, producing the R2 of 0.96 and 0.89, respectively. Sensitivity analysis determined that the percentage of CaO had the largest effect on geopolymer workability of 27.13%. Fly ash content had the largest effect on compressive strength of 34.44%. Our approach offers a straightforward and dependable strategy for designing and optimizing fly ash–based geopolymers.

Uncontrolled Keywords

• geopolymer
• fly ash
• machine learning
• workability
• compressive strength