Development and strength prediction of sustainable concrete having binary and ternary cementitious blends and incorporating recycled aggregates from demolished UAE buildings: Experimental and machine learning-based studies

dc.contributor.authorAl Martini, Samer
dc.contributor.authorSabouni, Reem
dc.contributor.authorKhartabil, Ahmad
dc.contributor.authorETAL..
dc.date.accessioned2024-02-23T07:34:53Z
dc.date.available2024-02-23T07:34:53Z
dc.date.issued2023-05-30
dc.descriptionThe construction industry is resource and energy intensive and wasteful, thus, plays a pivotal role in climate change.
dc.description.abstractThis study investigates the mechanical properties of concrete mixes containing recycled concrete aggregate (RCA) from demolished buildings in Abu Dhabi, aiming to promote sustainable construction practices. Ground granulated blast-furnace slag and fly ash were used as supplementary cementitious materials in 70 concrete mixes, incorporating varying RCA replacement levels (0%, 20%, 40%, 60%, and 100%). Uniaxial compressive and flexural tests were conducted, revealing that concrete with 20% RCA can be utilized in structural applications, as its strength exceeded 45 MPa. Most ternary blend mixes achieved the target design strength, excluding 100% RCA mixes. Analysis of variance evaluated the significance of strength differences across RCA levels, and accurate machine learning-based models were developed for predicting the compressive and flexural strengths of eco-friendly concrete containing RCA. The findings encourage wider adoption of RCA in structural applications, contributing to more sustainable concrete practices in the construction industry. Keywords: Recycled aggregates, Strength, Sustainability, Supplementary cementitious materials, Circular economy, Machine learning
dc.identifier.citationAl Martini, S., Sabouni, R., Khartabil, A., Wakjira, T. G., & Alam, M. S. (2023). Development and strength prediction of sustainable concrete having binary and ternary cementitious blends and incorporating recycled aggregates from demolished UAE buildings: Experimental and machine learning-based studies. Construction and Building Materials, 380, 131278.
dc.identifier.doihttps://doi.org/10.1016/j.conbuildmat.2023.131278
dc.identifier.urihttps://dspace.adu.ac.ae/handle/1/1283
dc.language.isoen
dc.publisherElsevier
dc.titleDevelopment and strength prediction of sustainable concrete having binary and ternary cementitious blends and incorporating recycled aggregates from demolished UAE buildings: Experimental and machine learning-based studies
dc.typeArticle

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