Behavioral assessment of graphene nanoplatelets reinforced concrete beams by experimental, statistical, and analytical methods. (December 2022)

• Record Type: Journal Article
• Authors: Ismail, Fouad Ismail; Shafiq, Nasir; Abbas, Yassir M.; Bheel, Naraindas; Benjeddou, Omrane; Ahmad, Mahmood; Sabri, Mohanad Muayad; Ateya, El Sayed
• Is Part Of: Case studies in construction materials. Volume 17(2022)
• Publication Date: 2022-12
• Subjects: Graphene nanoplatelets -- Flexural performance -- Post-cracking ductility -- Mechanical properties -- High-strength concrete; Building materials -- Case studies -- Periodicals; 691.05
• DOI: 10.1016/j.cscm.2022.e01676
• Languages: English
• ISSNs: 2214-5095

Abstract

Abstract: 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.