Prediction of flexural and split tensile strength of waste glass-concrete composite using machine learning algorithms
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Date
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Please read abstract in the article.
HIGHLIGHTS
• Machine learning models predict the mechanical properties of concrete-glass composite.
• Characteristics of glass.
• Mechanical properties of concrete-glass composite.
• Methodological innovation for robust machine learning models to optimize materials for sustainable construction.
Description
Keywords
Waste glass concrete, Machine learning, Adaptive boosting (AdaBoost), Extreme gradient boosting (XGBoost), Light gradient boosting machine (LightGBM), Gaussian process, Waste material, Support vector regression (SVR)
Sustainable Development Goals
SDG-09: Industry, innovation and infrastructure
Citation
Mirindi, D., Sinkhonde, D., Bezabih, T. et al. 2026, 'Prediction of flexural and split tensile strength of waste glass-concrete composite using machine learning algorithms', Green Technologies and Sustainability, vol. 4, art. 100275, pp. 1-27, doi : 10.1016/j.grets.2025.100275.