Numerical and theoretical investigation on the constitutive model of graphene-enhanced FRCM composite
Numerical and theoretical investigation on the constitutive model of graphene-enhanced FRCM composite
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DOI：
10.1016/j.jobe.2024.108734
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发表时间：
2024-02-09
影响因子：
6.4
通讯作者：
Ueda，Tamon
中科院分区：
文献类型：
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作者：
Zhu，Xiaoming;Su，Meini;Ueda，Tamon
FRCM复合材料的局限性之一是水泥基基质不能充分浸渍到纤维束中。发现石墨烯能够增加浸渍深度，从而增加FRCM复合材料中有效纤维的比例。随着有效纤维比例的增加，FRCM复合材料本构模型的准确程度对FRCM加固钢筋混凝土梁的设计影响越来越大。本研究旨在了解石墨烯增强纤维增强水泥基复合材料（Gr-FRCM）的拉伸行为，并开发一个精确的简化本构模型的设计计算。首先，建立并验证了Gr-FRCM复合材料板单轴拉伸的数值模型。之后，进行了参数研究，调查材料性能，纤维-基体界面性能和纤维体积比的本构模型的影响。实验和数值结果被用来评估现有的不同的本构模型的准确性，并确定本构模型的关键参数，需要改进。提出了作出上述改进的建议。最后，一个工作例子来证明现有模型的不同程度的精度取决于渗透深度，以及所提出的模型的良好精度。
One of the limitations of FRCM composite is insufficient impregnation of the cementitious matrix into fibre bundles. Graphene is found to be able to increase the impregnation depth, thereby increasing the proportion of effective fibres in the FRCM composite. As the proportion of effective fibres increases, the level of accuracy of the constitutive model of FRCM composite has more impact on the design of FRCM strengthened reinforced concrete beams. This study aims to understand the tensile behaviour of Graphene-enhanced Fibre Reinforced Cementitious Matrix (Gr-FRCM) composite and develops an accurate simplified constitutive model for design calculations. Firstly, a numerical model of Gr-FRCM composite plates subjected to uniaxial tension was developed and validated. Afterwards, a parametric study was conducted to investigate the effects of material properties, fibre-matrix interfacial properties and fibre volume ratio on the constitutive model. Both experimental and numerical results were used to assess the accuracy of different existing constitutive models and to identify the key parameters of the constitutive models that require improvement. Proposals to make the aforementioned improvements are suggested. Finally, a work example is presented to demonstrate the varying degrees of accuracy of existing models depending on the penetration depth, and good accuracy of the proposed model.