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.author | Al Martini, Samer | |
| dc.contributor.author | Sabouni, Reem | |
| dc.contributor.author | Khartabil, Ahmad | |
| dc.contributor.author | ETAL.. | |
| dc.date.accessioned | 2024-02-23T07:34:53Z | |
| dc.date.available | 2024-02-23T07:34:53Z | |
| dc.date.issued | 2023-05-30 | |
| dc.description | The construction industry is resource and energy intensive and wasteful, thus, plays a pivotal role in climate change. | |
| dc.description.abstract | This 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.citation | Al 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.doi | https://doi.org/10.1016/j.conbuildmat.2023.131278 | |
| dc.identifier.uri | https://dspace.adu.ac.ae/handle/1/1283 | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.title | 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.type | Article |
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