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Nonlinear finite-element analysis for the behavior prediction and strut efficiency factor of GFRP-reinforced concrete deep beams

机译:GFRP加固混凝土深梁的行为预测及支撑效率系数的非线性有限元分析

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The recent experimental results and proposed strut-and-tie model (STM) for deep beams reinforced entirely with glass-fiber-reinforced polymer (GFRP) bars have suggested that a comprehensive examination is required to improve the strut efficiency factor and its affecting parameters. This study uses nonlinear finite-element analysis (FEA) to perform an in-depth investigation. FEA response was compared against the experimental results in terms of crack patterns, failure modes, strains in reinforcement and concrete, and load deflection relationships. The results show that the simulation procedures employed were stable and compliant, and that they provided reasonably accurate simulations of the behavior. FEA was used to confirm some hypotheses associated with the experimental investigations. A comprehensive parametric study was conducted to investigate the effect of Web reinforcement and loading plate size on the strut efficiency factor. It was shown that vertical Web reinforcement has no clear effect on the strength, but it is required for crack control. On the other hand, horizontal web reinforcement should be accompanied with vertical reinforcement. Loading-plate size showed a clear effect on the deep-beam strength. Based on the numerical simulation results, a modification to a recently proposed STM is suggested. The modified STM was compared to available STMs in design codes and provisions, yielding better correlation with experimental results. (C) 2017 Elsevier Ltd. All rights reserved.
机译:最近的实验结果和拟议的用玻璃纤维增​​强聚合物(GFRP)筋完全加固的深梁的压杆模型(STM)建议,需要进行全面检查以提高压杆效率系数及其影响参数。本研究使用非线性有限元分析(FEA)进行了深入的研究。在裂纹模式,破坏模式,钢筋和混凝土中的应变以及荷载挠度关系方面,将FEA响应与实验结果进行了比较。结果表明,所采用的仿真程序稳定且合规,并且它们为行为提供了合理准确的仿真。 FEA被用来确认与实验研究有关的一些假设。进行了全面的参数研究,以研究腹板加固和承压板尺寸对支撑效率系数的影响。结果表明,垂直腹板加固对强度没有明显的影响,但是裂纹控制是必需的。另一方面,水平腹板加强应与垂直加强一起进行。加载板的尺寸对深梁强度显示出明显的影响。基于数值模拟结果,建议对最近提出的STM进行修改。修改后的STM在设计规范和规定中与可用STM进行了比较,从而与实验结果具有更好的相关性。 (C)2017 Elsevier Ltd.保留所有权利。

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