首页> 外文期刊>Journal of Civil Engineering and Management >PREDICTIVE MODEL TO THE BOND STRENGTH OF FRPTOCONCRETE UNDER DIRECT PULLOUT USING GENE EXPRESSION PROGRAMMING
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PREDICTIVE MODEL TO THE BOND STRENGTH OF FRPTOCONCRETE UNDER DIRECT PULLOUT USING GENE EXPRESSION PROGRAMMING

机译:基因表达规划直接拉出下Frptoconcrete粘合强度的预测模型

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摘要

Gene expression programming (GEP) is used in this research to develop an empirical model that predicts the bond strength between the concrete surface and carbon fiber reinforced polymer (CFRP) sheets under direct pull out. Therefore, a large and reliable database containing 770 test specimens is collected from the literature. The gene expression programming model is developed using eight parameters that predominantly control the bond strength. These parameters are concrete compressive strength, maximum aggregate size, fiber reinforced polymer (FRP) tensile strength, FRP thickness, FRP modulus of elasticity, adhesive tensile strength, FRP length, and FRP width. The model is validated using the experimental results and a statistical assessment is implemented to evaluate the performance of the proposed GEP model. Furthermore, the predicted bond results, obtained using the GEP model, are compared to the results obtained from several analytical models available in the literature and a parametric study is conducted to further ensure the consistency of the model by checking the trends between the input parameters and the predicted bond strength. The proposed model can reasonably predict the bond strength that is most fitting to the experimental database compared to the analytical models and the trends of the GEP model are in agreement with the overall trends of the analytical models and experimental tests.
机译:基因表达编程(GEP)用于该研究以开发一种经验模型,其预测在直接拉出下的混凝土表面和碳纤维增强聚合物(CFRP)片之间的粘合强度。因此,从文献中收集包含770个测试样品的大型可靠的数据库。基因表达编程模型是使用八个参数开发的,这些参数主要控制粘合强度。这些参数是混凝土抗压强度,最大骨料尺寸,纤维增强聚合物(FRP)拉伸强度,FRP厚度,弹性模量,粘性拉伸强度,FRP长度和FRP宽度。使用实验结果验证该模型,实施统计评估以评估提出的GEP模型的表现。此外,使用GEP模型获得的预测键结果与从文献中可用的几种可用的分析模型获得的结果进行比较,并且进行参数研究以通过检查输入参数之间的趋势来进一步确保模型的一致性预测的债券强度。与分析模型相比,所提出的模型可以合理地预测对实验数据库最适合的粘合强度,并且GEP模型的趋势与分析模型和实验测试的整体趋势一致。

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