From Machine Learning to Empirical Modelling: A Structured Framework for Predicting Compressive Strength of Fly Ash-Based Geopolymer Concrete

Fly ash-based geopolymer concrete (FAGC) is a sustainable alternative to Portland cement concrete, offering significant reductions in carbon emissions while maintaining sufficient strength. This study proposes a three-stage framework for developing empirical formulae to accurately and interpretably predict FAGC compressive strength. In the first stage, predictive models were developed using linear regression (LR), deep neural network (DNN), and residual neural network (ResNet) approaches. Among these, the ResNet model achieved the highest predictive accuracy and effectively captured the complex nonlinear relationship between mix components, curing conditions, and compressive strength. In the second stage, global sensitivity analysis identified sodium silicate content, curing time, sodium hydroxide molarity, and water content as the most influential variables. Additionally, the interaction between fine aggregate content and curing temperature was found to have a substantial effect on strength development. In the final stage, an empirical formula was developed based on key variables and their interactions, providing a simple yet reliable tool for practical strength prediction with reduced computational requirements. The proposed framework is expected to bridge the gap between machine-learning prediction and applicability to support mix design optimisation and promote the wider adoption of sustainable geopolymer concrete in construction applications.