S. Hamad

Novel Interpretable Machine Learning Models for Predicting Compressive Strength of Nano-Silica Concrete

This study provides a robust, interpretable, and generalizable ML framework for optimizing nano-silica concrete mix design and highlights the strong potential of ML, particularly ensemble models combined with explainable AI techniques, to improve prediction reliability, reduce trial-and-error experimentation, and support more cost-efficient and sustainable concrete design.