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

dc.contributor.authorMirindi, Derrick
dc.contributor.authorSinkhonde, David
dc.contributor.authorBezabih, Tajebe
dc.contributor.authorMirindi, Frederic
dc.contributor.authorOshineye, Oluwakemi
dc.contributor.authorMirindi, Patrice
dc.date.accessioned2026-03-19T10:20:17Z
dc.date.available2026-03-19T10:20:17Z
dc.date.issued2026-04
dc.description.abstractPlease 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.
dc.description.departmentAgricultural Economics, Extension and Rural Development
dc.description.librarianhj2026
dc.description.sdgSDG-09: Industry, innovation and infrastructure
dc.description.urihttps://www.keaipublishing.com/en/journals/green-technologies-and-sustainability/
dc.identifier.citationMirindi, 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.
dc.identifier.issn2949-7361 (online)
dc.identifier.other10.1016/j.grets.2025.100275
dc.identifier.urihttp://hdl.handle.net/2263/109077
dc.language.isoen
dc.publisherElsevier
dc.rights© 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/).
dc.subjectWaste glass concrete
dc.subjectMachine learning
dc.subjectAdaptive boosting (AdaBoost)
dc.subjectExtreme gradient boosting (XGBoost)
dc.subjectLight gradient boosting machine (LightGBM)
dc.subjectGaussian process
dc.subjectWaste material
dc.subjectSupport vector regression (SVR)
dc.titlePrediction of flexural and split tensile strength of waste glass-concrete composite using machine learning algorithms
dc.typeArticle

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