biostudies-literatureHsu CHDepartment of Industrial Technology, Ministry of Economic AffairsResource Circulation Administration Ministry of Environment3141https://www.ebi.ac.uk/biostudies/studies/S-EPMC11242840biostudies-literatureUnknown17(13)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 strengthMaterials (Basel, Switzerland)Efficient Compressive Strength Prediction of Alkali-Activated Waste Materials Using Machine Learning.PMC11242840P454L03100Liu TYChiu KCLiu CFHsu CHChang MHChan HYfalseEfficient Compressive Strength Prediction of Alkali-Activated Waste Materials Using Machine Learning.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 strength2024-01-01T00:00:00Z2024 Jun2025-07-10T03:08:50.698Z2025-04-06T18:03:58.219ZS-EPMC112428403899822410.3390/ma17133141