Experimental Verification for Machine-Learning Approaches in Compressive Strength Prediction of Alkali-Activated Concrete

This study presents a new tool for predicting the compressive strength of alkali-activated concrete (AAC) based on its binder mineralogy. It was made using a machine-learning (ML) framework. To achieve this challenging task, the authors collected 45 data sources from the literature to build a data set of 809 samples that included nine effective features, such as binder content, alkaline-to-binder ratio, binder chemical compositions (CaO, SiO2, Al2O3, and MgO contents), NaOH molarity and percentage in alkaline solution, age, and compressive strength. To assess the accuracy of the prediction tool, the authors trained and evaluated the data set using the most relevant ML methods: Lasso regression, random forest regression, decision tree, AdaBoost, extreme gradient boosting (XGB), and long short-term memory with recurrent neural network (LSTM-RNN). An experimental program was also conducted using the ML approaches to further validate the accuracy of the predictions. Overall, the XGB and LSTM-RNN methods were observed to significantly outperform the other methods in terms of accuracy when predicting compressive strength. Particularly impressive results were seen, with R2 values of 0.93 and 0.96 for compressive strength prediction being recorded. Further analysis of the binder mineralogy showed that increasing calcite content percentage led to an increase in AAC compressive strength, whereas increasing silicates in the binder mineralogy caused a decrease in AAC compressive strength due to the shortage of calcites. The Shapley additive explanations (SHAP) analysis revealed that calcite and silicate had the highest SHAP values for the AAC compressive strength. In contrast, the Al2O3 and MgO percentages had only a minor impact on the compressive strength of AAC.