Compressive strength prediction of fly ash/slag-based geopolymer concrete using EBA-optimised chemistry-informed interpretable deep learning model

Geopolymer concrete (GPC) is a sustainable alternative to conventional Portland cement concrete, utilising industrial by-products like fly ash (FA) and ground-granulated blast-furnace slag (GGBS). However, optimising GPC's compressive strength (CS) often requires costly and time-consuming experimental trials. This study develops a deep learning (DL) model based on convolutional neural networks (CNN) to predict the CS of FA/GGBS-based GPC. The model integrates key mix parameters such as material proportions, curing conditions, and the chemical composition of FA/GGBS binders, making it chemistry-informed. The CNN architecture includes two convolution layers, global max-pooling, and two fully connected layers, with 11 input variables and a single output for CS prediction. To optimise model accuracy, the enhanced bat algorithm (EBA) is designed for metaparameter tuning. The model is trained and tested on a comprehensive dataset comprising experimental data extracted from published literature. The results demonstrate that the EBA-optimised CNN outperforms traditional learning models, including support vector machine (SVM), extreme gradient boosting (XGBoost), and artificial neural networks (ANN), with higher performance in terms of R2, MAE, and RMSE. The model achieved R2 values of 0.997 for training and 0.978 for testing. Additionally, the Shapley additive explanations (SHAP) method was used to interpret the model, identifying the Na2O to binder ratio and curing age as the most influential factors on CS. This study highlights the potential of DL techniques, particularly chemistry-informed CNN with metaparameter optimisation, for accurately predicting the strength of GPC, providing a cost-effective solution for mix design and performance evaluation.