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Prediction of FRP-confined compressive strength of concrete using artificial neural networks

机译:FRP约束的混凝土抗压强度的人工神经网络预测

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摘要

Strengthening and retrofitting of concrete columns by wrapping and bonding FRP sheets has become an efficient technique in recent years. Considerable investigations have been carried out in the field of FRP-confined concrete and there are many proposed models that predict the compressive strength which are developed empirically by either doing regression analysis using existing test data or by a development based on the theory of plasticity. In the present study, a new approach is developed to obtain the FRP-confined compressive strength of concrete using a large number of experimental data by applying artificial neural networks. Having parameters used as input nodes in ANN modeling such as characteristics of concrete and FRP, the output node was FRP-confined compressive strength of concrete. The idealized neural network was employed to generate empirical charts and equations for use in design. The comparison of the new approach with existing empirical and experimental data shows good precision and accuracy of the developed ANN-based model in predicting the FRP-confined compressive strength of concrete.
机译:近年来,通过包裹和粘合FRP板来加强和改造混凝土柱已成为一种有效的技术。在FRP约束混凝土领域已经进行了相当多的研究,并且提出了许多预测抗压强度的模型,这些模型是通过使用现有测试数据进行回归分析或基于可塑性理论的发展而凭经验开发的。在本研究中,开发了一种新方法,通过使用大量人工数据,通过应用人工神经网络来获得FRP约束的混凝土抗压强度。将参数用作ANN建模中的输入节点(例如混凝土特性和FRP),输出节点为FRP约束的混凝土抗压强度。理想化的神经网络用于生成经验图和方程式,供设计中使用。新方法与现有经验和实验数据的比较表明,所开发的基于ANN的模型在预测FRP约束的混凝土抗压强度方面具有良好的精度和准确性。

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