Thu-Hien Thi Hoang

Interpretable Machine Learning Framework for Predicting Air Void Content in Sustainable Steel-Slag SMA Mixtures Using Metaheuristic-Optimized XGBoost

An interpretable machine learning framework for predicting Va using 74 mixtures collected from 16 published studies and nine mixture-related variables is developed, which provides an interpretable tool for preliminary Va estimation and mixture screening, while independent laboratory and field validation remains necessa...