Efficient Compressive Strength Prediction of Alkali-Activated Waste Materials Using Machine Learning
Chien-Hua Hsu,
Hao-Yu Chan,
Ming-Hui Chang
et al.
Abstract:This study explores the integration of machine learning (ML) techniques to predict and optimize the compressive strength of alkali-activated materials (AAMs) sourced from four industrial waste streams: blast furnace slag, fly ash, reducing slag, and waste glass. Aimed at mitigating the labor-intensive trial-and-error method in AAM formulation, ML models can predict the compressive strength and then streamline the mixture compositions. By leveraging a dataset of only 42 samples, the Random Forest (RF) model und… Show more
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