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Study on the structural damage identification method with combined parameters based on RBF neural network

机译:基于RBF神经网络的组合参数结构损伤识别方法研究

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Localized damage in a structure affects its dynamic properties. The change is characterized by changes in the eigenparameters such as natural frequencies, the mode shapes associated with each natural frequency etc. and much work has been undertaken by investigating the single parameter which is assigned as input parameter to neural network to determine the damage location and the damage size. In this paper, a structural damage identification method with combined parameters based on RBF neural network is presented. To overcome the disadvantages of single input parameter, combined parameters, which are obtained by combining natural frequencies, mode shape data and changes in curvature mode shape, are assigned as input parameters to neural network. The simulation results to a lumped-mass system of six degrees show that the method is effective and applicable.
机译:结构中的局部损坏会影响其动态特性。这种变化的特征在于固有参数的变化,例如固有频率,与每个固有频率相关的模态形状等。通过研究作为输入参数分配给神经网络的单个参数来确定损伤位置并进行了大量工作。伤害的大小。提出了一种基于RBF神经网络的组合参数结构损伤识别方法。为了克服单一输入参数的缺点,将通过组合固有频率,模式形状数据和曲率模式形状的变化而获得的组合参数作为输入参数分配给神经网络。对六度集总质量系统的仿真结果表明,该方法是有效的和适用的。

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