Prediction of flexural and split tensile strength of waste glass-concrete composite using machine learning algorithms

Mirindi, DerrickSinkhonde, DavidBezabih, TajebeMirindi, FredericOshineye, OluwakemiMirindi, Patrice2026-03-192026-03-192026-04Mirindi, 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.2025.100275.2949-7361 (online)10.1016/j.grets.2025.100275http://hdl.handle.net/2263/109077Please 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.en© 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Waste glass concreteMachine learningAdaptive boosting (AdaBoost)Extreme gradient boosting (XGBoost)Light gradient boosting machine (LightGBM)Gaussian processWaste materialSupport vector regression (SVR)Prediction of flexural and split tensile strength of waste glass-concrete composite using machine learning algorithmsArticle