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The local bond mechanics of glass-fiber reinforced polymer (GFRP) bars in normal strength concrete was investigated through experimental testing and analytical modeling. The experimental program was comprised of 30 direct tension pullout specimens with short anchorages. A novel test setup, specially designed so as to minimize the spurious influence of testing conditions on measured bond properties was adopted in the study. Parameters considered were the bar roughness and diameter, the size effect expressed by the constant cover to bar diameter ratio, and the external confining pressure exerted over the anchorage length by transverse externally bonded FRP sheets. Results of the study were summarized in the form of local bond-slip curves, whereby performance limit states were quantified by the amount of loaded end slip and bond strength. An analytical model of the bond stress-slip response of a GFRP bar was derived from first principles and calibrated against the test data of the present investigation. Using the calibrated model, design values for bond and slip were estimated with reference to the code limit state model for bond. 相似文献
3.
Stress concentration and fatigue of profiled reinforcing steels 总被引:1,自引:0,他引:1
Stress concentrations arise from profiles of ribbed reinforcing steel bars and in this study the results of calculated stress concentration factors (SCF), by using finite element method, are related to the fatigue test results. It is apparent that the degree of confinement of a ribbed bar embedded in concrete not only affects the magnitude of the ultimate bond stress but also the bond stress-slip relation. Thus the rib geometry or profile pattern is designed for optimum bond characteristics and not for fatigue considerations. However the rib geometry influences the fatigue performance through the SCFs arising from the root radius, width and flank angles of the profiles. It is shown that these latter factors have a significant influence on the fatigue behaviour of reinforcing steel bars. 相似文献
4.
为了考虑剪切变形对玻璃纤维增强复合材料(GFRP)构件屈曲荷载的影响,利用Engesser剪切变形理论,推导考虑剪切变形和初弯曲的临界荷载. 应用于GFRP圆管时,考虑材料各向异性特征对剪切系数及强度的影响. 对4根具有不同长细比的GFRP圆管试件进行轴压试验. 结果表明,GFRP圆管在截面强度估算时应综合考虑轴向纤维压缩破坏和环向基体拉伸破坏. 所推导的临界荷载计算式的结果和试验结果吻合良好. 相似文献
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本文应用细观力学、经典层板理论、平截面假定、FRP约束混凝土本构模型及数值积分对GFRP管混凝土组合结构压弯构件进行了非线性全过程分析。通过理论计算发现GFRP管混凝土组合结构能有效地提高构件的承载力,且构件具有良好的变形能力。FRP壳体参与承载且有效地约束混凝土的变形,显著提高核心混凝土强度和变形能力。文中讨论了轴压比、长细比、径厚比、混凝土强度等级、纵筋配筋率、铺层角变化对极限水平荷载和极限水平位移的影响,从而得到了一些有益的结论。 相似文献
7.
在基体树脂中添加触变剂白炭黑,制成了S-94触变性不饱和聚酯树脂。该树脂的触变指数比基体树脂高3倍多,改善了不饱和聚酯树脂的流变性。 相似文献
8.
结合实例剖析由产品结构、成型工艺和工艺装备等主要因素影响的复合夹层结构玻璃钢件和实体玻璃钢件在固化成型中的变形现象,并指出了预防变形的各相应有效措施。 相似文献
9.
通过现场拉拔破坏性试验,测得不同直径的GFRP抗浮锚杆在基础底板内的极限承载力和滑移量,并与实际工程中不同形式的钢筋抗浮锚杆作比较,分析其承载性能和粘结特性。研究表明,在相同的混凝土强度与养护条件下,相同直径的GFRP抗浮锚杆的极限承载力、平均粘结强度与钢筋抗浮锚杆相比较高,且GFRP抗浮锚杆的变形能够满足实际工程需求,充分验证了GFRP材料用作抗浮锚杆的先进性与合理性。基于试验结果与理论分析,给出了GFRP抗浮锚杆与基础底板的最佳锚固面积,并提出了计算公式。 相似文献
10.
The challenges of machining, particularly milling, glass fibre-reinforced polymer (GFRP) composites are their abrasiveness (which lead to excessive tool wear) and susceptible to workpiece damage when improper machining parameters are used. It is imperative that the condition of cutting tool being monitored during the machining process of GFRP composites so as to re-compensating the effect of tool wear on the machined components. Until recently, empirical data on tool wear monitoring of this material during end milling process is still limited in existing literature. Thus, this paper presents the development and evaluation of tool condition monitoring technique using measured machining force data and Adaptive Network-Based Fuzzy Inference Systems during end milling of the GFRP composites. The proposed modelling approaches employ two different data partitioning techniques in improving the predictability of machinability response. Results show that superior predictability of tool wear was observed when using feed force data for both data partitioning techniques. In particular, the ANFIS models were able to match the nonlinear relationship of tool wear and feed force highly effective compared to that of the simple power law of regression trend. This was confirmed through two statistical indices, namely r2 and root mean square error (RMSE), performed on training as well as checking datasets. 相似文献