Enhancing the mechanical and environmental performance of engineered cementitious composite with metakaolin, silica fume, and graphene nanoplatelets
Engineering cementitious composites are designed to offer high ductility, enhanced strength, and enhanced impact resistance as rehabilitation materials for various support structures and equipment. ECC is an inventive material for construction with tensile strain hardened properties and multiple fra...
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Main Authors: | , , , , , , |
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Format: | Article |
Published: |
Elsevier Ltd
2023
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Online Access: | http://scholars.utp.edu.my/id/eprint/37291/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170646770&doi=10.1016%2fj.conbuildmat.2023.133187&partnerID=40&md5=ccf620d818bc297feec2d15399c19988 |
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Summary: | Engineering cementitious composites are designed to offer high ductility, enhanced strength, and enhanced impact resistance as rehabilitation materials for various support structures and equipment. ECC is an inventive material for construction with tensile strain hardened properties and multiple fracture behaviors, which is highly encouraged in the construction industry. The objective of this study was to create an eco-efficient engineered cementitious composite (ECC) with binary systems of cement with metakaolin (MK) and silica fume (SF) and an optimized dosage of graphene nanoplatelets (GNPs). To optimize mixture proportion, an experimental design was developed using the response surface method. We analyzed the compressive strength, split tensile strength, flexural strength, dried density, embodied carbon, and eco-strength efficiency of the optimal mixture proportion to determine its mechanical properties and low-carbon footprint. Results analysis showed a total of 10 content of SF and MK is optimum for an eco-friendly (low carbon profile) and mechanically strong ECC mixture. If it exceeds 10, the mechanical strength of the composite decreases. Furthermore, 0.02 to 0.03 GNP concentration improved mechanical characteristics even further. According to the results of the multi-objective optimization, the optimal values for the variables Mk, SF, and GNPs are 9.19, 4.83, and 0.03, respectively. © 2023 Elsevier Ltd |
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