Assessment of Hot-Water-Alkali Treated Bagasse Fiber in Metakaolin-Based Geopolymer Using Machine Learning

This study aims to develop metakaolin-based geopolymers reinforced with sugarcane bagasse fiber (BF) and to evaluate the effect of BF treatment on the composite's performance. The BF is pretreated with hot water and sodium hydroxide before being incorporated into the geopolymer matrix. Metakaolin-based geopolymer specimens containing 0%, 3%, 4%, and 5% BF by weight are prepared, and their mechanical properties and water absorption are analyzed. Scanning electron microscopy and Fourier transform infrared spectroscopy analyses reveal that the combined hot-water-alkali treatment significantly modifies the fiber surface. The treatment removes impurities and increases surface roughness, thereby enhancing fiber–matrix bonding. As a result, this treatment improves compressive and splitting tensile strength (STS) while reducing water absorption compared to untreated BF. Furthermore, machine learning algorithms, including random forest, AdaBoost, and XGBoost, are applied to predict STS. Among the three models, XGBoost demonstrates the highest predictive accuracy (R^² = 0.95, MAE = 0.28), indicating reliable predictions of mechanical strength.