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

• Published in: Materials Vol. 17; no. 13; p. 3141
• Main Authors: Hsu, Chien-Hua, Chan, Hao-Yu, Chang, Ming-Hui, Liu, Chiung-Fang, Liu, Tzu-Yu, Chiu, Kuo-Chuang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.06.2024
• Subjects: Algorithms; alkali-activated materials (AAMs); Alkalinity; Blast furnace slags; component classification; Compressive strength; Concrete; Data processing; Datasets; Design; Fly ash; Formulations; Industrial wastes; Laboratories; Machine learning; Materials science; optimization; Raw materials; Recipes; Slag; Trial and error methods; Waste management; Waste materials; Taiwan; compressive strength; component classification; machine learning; optimization; alkali-activated materials (AAMs)
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma17133141

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 was achieved with the RF model, improving the model accuracy from 0.05 to 0.62. Experimental validation further confirmed the ML model's efficacy, as the formulations ultimately achieved the desired strength threshold, with a significant 59.65% improvement over the initial experiments. Additionally, the fact that the recommended formulations using ML methods only required about 5 min underscores the transformative potential of ML in reshaping AAM design paradigms and expediting the development process.