Explainable Hybridized Machine Learning for Prediction of Compressive Strength of Fly-Ash based Geopolymer Concrete

Abstract

This study utilizes robust machine learning (ML) techniques to predict compressive strength of fly-ash-based geopolymer concrete (GPC), a sustainable replacement for traditional concrete. Popular ensemble models, specifically Random Forest (RF) and Extreme Gradient Boosting (XGB), were taken as base models and were hybridized using metaheuristic algorithms (Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) for hyperparameter optimization. A 5 × 5 nested cross-validation (nCV) approach was adopted, where inner folds were used for hyperparameter tuning, and outer folds for unbiased performance evaluation for the limited 273-sample dataset. The findings revealed that GWO-XGB outperformed other hybridized processes, with aggregated R2 and RMSE of 0.9661 ± 0.011 and 3.0401 ± 0.5363, respectively, in the testing phases. The performance ranking for both the training and testing phases was: GWO-XGB > PSO-XGB > GWO-RF > PSO-RF. Further, SHAP analysis was performed on models obtained from the best-tuning process, which identified Curing Period (CTP) and Curing Temperature (CTR) as the most critical parameters influencing FA-GPC strength. The best-optimized model was also used to build a graphical user interface (GUI). This work offers a reliable framework, not only for predicting CS but also for demonstrating how feature interactions contribute to strength development, providing insights into effective ML use in concrete technology.