Using ensemble machine learning and metaheuristic optimization for modelling the elastic modulus of geopolymer concrete

Geopolymer concrete emerges as a sustainable and durable alternative to conventional concrete, addressing its high carbon footprint and enhanced durability. The distinct properties of geopolymer concrete, governed by supplementary cementitious materials and alkaline activators, promise reduced environmental impact and improved structural resilience. However, its complex composition complicates the prediction of mechanical properties such as the elastic modulus, crucial for structural applications. This study introduces an innovative approach using the eXtreme Gradient Boosting (XGBoost) technique integrated with the multi-objective grey wolf optimizer to model the elastic modulus of geopolymer concrete. By dynamically selecting influential features and optimizing model accuracy, this methodology advances beyond traditional empirical models, which fail to capture the nonlinear interactions intrinsic to geopolymer concrete. Utilizing a comprehensive database gathered from extensive literature, 22 potential variables were examined that influence geopolymer concrete’s elastic modulus. After mitigating multicollinearity and optimizing hyperparameters via Bayesian optimization, six XGBoost models were developed with different combinations of input variables, revealing compressive strength and total water content as pivotal predictors. The findings illustrate the models’ precision, with the trade-off between prediction accuracy and model simplicity visualized through the relationship between the number of input variables and prediction error. The study culminates in a user-friendly graphical user interface that enables easy prediction of geopolymer concrete’s elastic modulus and fosters educational engagement. This interface, available online, underscores the practicality and accessibility of advanced machine learning predictions. Overall, this research not only provides a robust predictive framework for geopolymer concrete’s elastic modulus using optimized input variables but also enhances the understanding of its underlying determinants, contributing to the advancement of sustainable construction materials.