Novel hybrid machine learning framework for high-fidelity prediction of fly ash-based geopolymer concrete strength

The complex, non-linear relationships between mix-design parameters and the mechanical properties of geopolymer concrete (GPC) are not fully understood, presenting a fundamental scientific challenge for accurate strength prediction and mix optimization. This challenge hinders the widespread adoption of GPC, a sustainable alternative to conventional concrete. Existing machine learning models for GPC often lack generalizability and interpretability due to the limited availability of datasets and basic architectures. This research introduces T-BoostNet, a novel hybrid machine learning (ML) framework combining Transformer architectures with XGBoost, designed for superior accuracy and interpretability in predicting GPC compressive strength. Leveraging an unprecedented dataset of 1117 unique GPC mixtures from 77 diverse studies, T-BoostNet effectively captures intricate local and global feature interactions. T-BoostNet consistently outperformed five benchmark ML algorithms, achieving the highest R 2 = 0 . 848 ± 0 . 024 and MAE = 3 . 56 ± 0 . 30 MPa . SHAP analysis provided crucial interpretability, identifying curing period, water content in alkaline solution, specimen age, and curing temperature as the most influential factors. This framework advances sustainable construction by providing a reliable, interpretable tool that accelerates GPC adoption, reduces costly laboratory trials, and aligns with global low-carbon material goals. • Novel T-BoostNet model predicts GPC strength with superior accuracy. • Large, diverse dataset enhances model generalizability. • SHAP analysis reveals curing conditions as key strength drivers. • ML framework accelerates sustainable geopolymer concrete adoption.