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Title: Development of Sustainable Engineered Cementitious Composites Using Recycled Concrete Aggregates— Feasibility Study Based on Mechanical Properties

Author(s): Adeyemi Adesina and Sreekanta Das

Publication: Materials Journal

Volume: 118

Issue: 4

Appears on pages(s): 97-107

Keywords: : engineered cementitious composites (ECC); fly ash; mechanical properties; microsilica sand (MSS); recycled concrete (RC); sustainability index

Date: 7/1/2021

Abstract:
Engineered cementitious composites (ECC) are special fiber-reinforced cementitious composites with outstanding performance. However, the high cost and unavailability of the special sand (that is, microsilica sand [MSS]) used as the aggregate for such composites have limited its use and even made it impractical in some geographical locations. Therefore, there is a dire need to find alternative materials that can be used to replace MSS in ECC. This study was carried out to investigate the feasibility of using recycled concrete (RC) as an alternative aggregate, which is a much cheaper and more sustainable option as opposed to the conventional MSS currently used in ECC. Fly ash—the coal-based, thermal, plant-generated waste material—was incorporated as an alternative binder to partially replace the traditional binder, portland cement (PC), which is a large greenhouse emitter. Thus, the use of fly ash to replace a high volume of ECC would result in a reduction in the carbon footprint of ECC. The RC was used to replace the MSS in proportions ranging from 0 to 100% at an increment of 25%. The mechanical performance of the ECC mixtures was assessed in terms of the compressive, tensile, and flexural properties. The results obtained from this study showed that the use of RC as a partial replacement of MSS in ECC mixtures resulted in a satisfactory ECC mixture. However, at a replacement ratio of 75% and above, the performance of ECC may not be acceptable. The sustainability index assessment of the mixtures indicates that the use of RC as a replacement of up to 50% of MSS is optimum.


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