Performance prediction of sustainable geopolymer concrete incorporating biochar and nano-cellulose using machine learning models

This research examines the compressive strength performance of environmentally friendly geopolymer concrete prepared with fly ash, ground granulated blast furnace slag (GGBS), biochar, and nano, cellulose fibers, facilitated by a machine, learning, based strength prediction. 28 geopolymer mixes were prepared with fixed activators parameters and tested under both heat, curing and ambient, curing conditions at 7, 14, and 28 days. The experimental results augment that slag, rich mixes (>72% GGBS) in combination with moderate biochar content (46%) and nano, cellulose fibers (0.6–1.0%) reached compressive strengths of 60–61 MPa under ambient curing, which are equal to those of heat, cured systems. To facilitate rational mix optimization, machine learning models like Artificial Neural Networks (ANN), XGBoost, Random Forest, and Linear Regression were created using binder composition as input variables. Of these, ANN had the highest predictive accuracy (R = 0.87, MAE = 4.91 MPa), followed by XGBoost (R = 0.85). Residual analysis and ANOVA were used for statistical validation of the nonlinear models’ robustness. The results demonstrate that the use of bio, based additives in combination with ML, assisted prediction can produce high, strength ambient, cured geopolymer concrete, thus, providing a feasible solution for low, carbon and resource, efficient construction.