Efficient Compressive Strength Prediction of Alkali-Activated Waste Materials Using Machine Learning.

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 underwent fivefold cross-validation to ensure reliability. Despite challenges posed by the limited datasets, meticulous data processing steps facilitated the identification of pivotal features that influence compressive strength. Substantial enhancement in predicting compressive strength

SUBMITTER: Hsu CH 

PROVIDER: S-EPMC11242840 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature