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Mechanical performance enhancement of recycled aggregate concrete using GGBS and fly ash for sustainable construction

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Abstract

The pursuit of sustainable construction practices has led to a growing interest in the utilization of recycled materials in concrete production. To enhance the utilization of recycled aggregate concrete, its performance in terms of mechanical characteristics need to be optimized. This study investigates the effect of fly ash and ground granulated blast furnace slag (GGBS) as cementitious materials on the mechanical characteristics of recycled aggregate concrete. In this research, various combinations of recycled aggregate concrete were prepared with partial replacement cement by GGBS and fly ash. The mechanical characteristics including compressive strength, split tensile strength, and flexural strength tests are conducted on all concrete mixes and the results are compared to the control mix. The results of this study will help advance sustainable concrete technology and serve as a foundation for designing RAC for structural applications that incorporate recycled coarse aggregate, fly ash, and GGBS. The results confirm that the concrete mix with 40% recycled aggregate, 15% fly ash and 15% GGBS as cement replacements is optimum for enhancing the mechanical characteristics of concrete. The enhanced pozzolanic activity and filler effect of these fly ash and GGBS enhances the strength characteristics of recycled aggregate concrete. The findings contribute to sustainable construction practices by promoting the utilization of recycled materials and optimizing the properties of concrete mixtures.

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The authors confirm contribution to the paper as follows: study conception and design: VVSS, SSA data collection: PGK analysis and interpretation of results: KV draft manuscript preparation: KSS.

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Correspondence to Shaik Subhan Alisha.

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Sarma, V.V.S., Subhan Alisha, S., Vijay, K. et al. Mechanical performance enhancement of recycled aggregate concrete using GGBS and fly ash for sustainable construction. Multiscale and Multidiscip. Model. Exp. and Des. (2023). https://doi.org/10.1007/s41939-023-00271-9

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  • DOI: https://doi.org/10.1007/s41939-023-00271-9

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