Behavioral assessment of graphene nanoplatelets reinforced concrete beams by experimental, statistical, and analytical methods
The objective of this study was to evaluate the effect of graphene nanoplatelets (GnP) on the mechanical properties of concrete as well as the flexural performance of reinforced concrete (GnP-RC) beams. In the experimental campaign, several dosages of GnP (0.00, 0.02, 0.05, 0.10, 0.30, and 0.50 wt o...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Published: |
2022
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| Online Access: | http://scholars.utp.edu.my/id/eprint/34038/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141922974&doi=10.1016%2fj.cscm.2022.e01676&partnerID=40&md5=2ba73a1cc87017845fe8de08adb77abe |
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| Summary: | The objective of this study was to evaluate the effect of graphene nanoplatelets (GnP) on the mechanical properties of concrete as well as the flexural performance of reinforced concrete (GnP-RC) beams. In the experimental campaign, several dosages of GnP (0.00, 0.02, 0.05, 0.10, 0.30, and 0.50 wt of cement) were included in the concrete mixtures. First, the mechanical properties of concrete (compressive, tensile, flexural, and modulus of elasticity) were studied. A further experimental investigation was conducted on the flexural behavior of GnP-RC beams. The failure mode of beams, crack patterns, moment-curvature relationship, and ductility properties are reported. According to the observed results, GnP addition is capable of significantly improving mechanical properties. By adding 0.02 of GnP, both the compressive and tensile strengths were improved by 20.82 and 30.05, respectively. Additionally, 0.02 of GnP also enhanced the cracking, yielding, and ultimate loads of beams by 36, 23, and 15, respectively. Further, for the same concentration of GnP, the energy absorption and post-cracking ductility were improved by 25 and 20, respectively. This report also presents analytical and statistical models for predicting the ultimate moment capacity of RC beams containing nano-reinforcement materials. The models have been demonstrated to be accurate at predicting the present and independent data. © 2022 The Authors |
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