<p>Cementitious composites with a range of nanoparticles are unique materials with improved mechanical, microstructural, and durability qualities. Graphene oxide is one of the most promising nanomaterial’s used in civil engineering. This study was conducted to inspect the development of graphene oxide in cement and identify its mechanical and microstructural properties. Class f was used in this fly ash in addition to graphene to reduce the demand for cement. The first phase represents the replacement of fly ash in cement in five different percentages (from 5 to 25%) with an increment of 5% for every mix by the weight of the cement. The second phase indicates the addition of graphene oxide in cement with five different mixes which range from (0%, 0.02%, 0.04%, 0.06%, 0.08%, and 0.1%) of the weight of the cement. And finally, the combination of fly ash and graphene oxide is considered with a mix of (0.02–0.1%) plus the optimum percentage which is obtained in fly ash by the weight of the cement. The intended uses of the mixes and sets of mixes are while The cost and carbon footprint of concrete are reduced when fly ash is used in place of cement, while its resilience, toughness, are enhanced when graphene oxide (GO) is added in modest amounts. When fly ash and GO work together, concrete performs better in harsh settings, enabling engineers to optimize its qualities through methodical testing. The optimum percentage obtained for fly ash replacement was 20% with the enhancement of 17% for the graphene oxide and the maximum strength obtained in 0.08% with an increment of 22%. At the end of these phases, the optimum percentage was obtained in the mix of 0.08 + 20% which has the achievement of 38% strength. For the microstructural analysis, the characterization study was identified by using X-ray diffraction, scanning electron microscopy, Fourier-transform infrared spectroscopy, and Nano scratch test. The compressive strength results were predicted by using artificial neural network.</p>

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Enhancing fly ash utilization in cementitious composites using graphene oxide interfacial nano-engineering with artificial neural network

  • I. Ramana,
  • N. Parthasarathi

摘要

Cementitious composites with a range of nanoparticles are unique materials with improved mechanical, microstructural, and durability qualities. Graphene oxide is one of the most promising nanomaterial’s used in civil engineering. This study was conducted to inspect the development of graphene oxide in cement and identify its mechanical and microstructural properties. Class f was used in this fly ash in addition to graphene to reduce the demand for cement. The first phase represents the replacement of fly ash in cement in five different percentages (from 5 to 25%) with an increment of 5% for every mix by the weight of the cement. The second phase indicates the addition of graphene oxide in cement with five different mixes which range from (0%, 0.02%, 0.04%, 0.06%, 0.08%, and 0.1%) of the weight of the cement. And finally, the combination of fly ash and graphene oxide is considered with a mix of (0.02–0.1%) plus the optimum percentage which is obtained in fly ash by the weight of the cement. The intended uses of the mixes and sets of mixes are while The cost and carbon footprint of concrete are reduced when fly ash is used in place of cement, while its resilience, toughness, are enhanced when graphene oxide (GO) is added in modest amounts. When fly ash and GO work together, concrete performs better in harsh settings, enabling engineers to optimize its qualities through methodical testing. The optimum percentage obtained for fly ash replacement was 20% with the enhancement of 17% for the graphene oxide and the maximum strength obtained in 0.08% with an increment of 22%. At the end of these phases, the optimum percentage was obtained in the mix of 0.08 + 20% which has the achievement of 38% strength. For the microstructural analysis, the characterization study was identified by using X-ray diffraction, scanning electron microscopy, Fourier-transform infrared spectroscopy, and Nano scratch test. The compressive strength results were predicted by using artificial neural network.