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    Self-Sensing Carbon Fiber and Graphite–Epoxy Composites: Fabrication and Optimization for Strain-Monitoring Applications

    Source: Journal of Materials in Civil Engineering:;2025:;Volume ( 037 ):;issue: 003::page 04025009-1
    Author:
    Shanhong Wan
    ,
    Tao Zhou
    ,
    Zejiao Dong
    ,
    Shafi Ullah
    ,
    Yu Cao
    DOI: 10.1061/JMCEE7.MTENG-19038
    Publisher: American Society of Civil Engineers
    Abstract: Pavements are poorly monitored and repaired only after damage becomes apparent. This study focused on the self-sensing behavior of epoxy based conductive composites that allows the measurement of the strain distribution of pavements in real time. To do so, carbon fiber (CF) and graphite (G) filled composites were fabricated and optimally characterized to realize the self-sensing capability. The fabrication process involved the incorporation of CF and G through mechanical blending. Subsequently, a comprehensive evaluation was conducted to assess the electrical, microstructural, and mechanical properties of the resulting composites. It was observed that the composites with 3% CF and 5% G exhibited optimum compositions with a gauge factor of 33. Furthermore, strain sensing occurred as a dynamic process from the initial conditioning stage to achieving a stable electrical resistance baseline upon cyclic fatigue. The alignment of conductive fillers reflected the evolution of microstructure. The balance of reversible microstructural functions and degradation determined the durability of the composite. The outcomes are critical to predicting long-term self-sensing capability and ensuring reliable applications of epoxy composites in pavement health monitoring.
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      Self-Sensing Carbon Fiber and Graphite–Epoxy Composites: Fabrication and Optimization for Strain-Monitoring Applications

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4303892
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    contributor authorShanhong Wan
    contributor authorTao Zhou
    contributor authorZejiao Dong
    contributor authorShafi Ullah
    contributor authorYu Cao
    date accessioned2025-04-20T10:02:43Z
    date available2025-04-20T10:02:43Z
    date copyright1/8/2025 12:00:00 AM
    date issued2025
    identifier otherJMCEE7.MTENG-19038.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303892
    description abstractPavements are poorly monitored and repaired only after damage becomes apparent. This study focused on the self-sensing behavior of epoxy based conductive composites that allows the measurement of the strain distribution of pavements in real time. To do so, carbon fiber (CF) and graphite (G) filled composites were fabricated and optimally characterized to realize the self-sensing capability. The fabrication process involved the incorporation of CF and G through mechanical blending. Subsequently, a comprehensive evaluation was conducted to assess the electrical, microstructural, and mechanical properties of the resulting composites. It was observed that the composites with 3% CF and 5% G exhibited optimum compositions with a gauge factor of 33. Furthermore, strain sensing occurred as a dynamic process from the initial conditioning stage to achieving a stable electrical resistance baseline upon cyclic fatigue. The alignment of conductive fillers reflected the evolution of microstructure. The balance of reversible microstructural functions and degradation determined the durability of the composite. The outcomes are critical to predicting long-term self-sensing capability and ensuring reliable applications of epoxy composites in pavement health monitoring.
    publisherAmerican Society of Civil Engineers
    titleSelf-Sensing Carbon Fiber and Graphite–Epoxy Composites: Fabrication and Optimization for Strain-Monitoring Applications
    typeJournal Article
    journal volume37
    journal issue3
    journal titleJournal of Materials in Civil Engineering
    identifier doi10.1061/JMCEE7.MTENG-19038
    journal fristpage04025009-1
    journal lastpage04025009-13
    page13
    treeJournal of Materials in Civil Engineering:;2025:;Volume ( 037 ):;issue: 003
    contenttypeFulltext
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