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New empirical approaches for compressive strength assessment of CFRP confined rectangular concrete columns

机译:CFRP压缩强度评估的新实证方法狭窄矩形混凝土柱

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摘要

Utilizing of CFRP for confining concrete columns has been demonstrated to improve the capacity and ductility of columns. In reason of numerous parameters that affect the compressive strength of the CFRP confined rectangular concrete columns (CRCC), developing formula is complicated. In the current study, three methods are evaluated for predicting the residual compressive strength of CFRP CRCC. Multiple regressions (MR), stepwise regression (SR), and artificial neural network (ANN) models were extended as credible methods for generating and evaluating the compressive strength of CFRP CRCC. The required data for training algorithms obtained from a reliable database. The accuracy of the developed formulae is verified using appropriate criteria. Then, a comparison was made between proposed formulae-based models to examine the accuracy degree of these methods. It is understood that despite a negligible difference between proposed models results the obtained formulae based on the SR and ANN methods give the exact results than the MR model.
机译:已经证明了利用CFRP用于限制混凝土柱,以提高柱的容量和延展性。由于众多参数影响CFRP受限矩形混凝土柱(CRCC)的抗压强度,显影配方复杂。在目前的研究中,评估三种方法以预测CFRP CRCC的残余抗压强度。多元回归(MR),逐步回归(SR)和人工神经网络(ANN)模型被扩展为用于产生和评估CFRP CRCC的抗压强度的可信方法。从可靠数据库获得的培训算法所需的数据。使用适当的标准验证发达式公式的准确性。然后,在所提出的基于式的模型之间进行比较,以检查这些方法的准确度。据了解,尽管所提出的模型之间的差异可以忽略不计,但是基于SR和ANN方法的获得的公式给出了比MR模型的精确结果。

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