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Application of eigenvalue perturbation theory for detecting small structural damage using dynamic responses

机译：特征值摄动理论在结构小损伤动态响应检测中的应用

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摘要

Current methods for structural damage identification, such as genetic algorithms and artificial neural networks, are often implemented based on a few measured data and a large number of simulation data. The tremendous time-consuming computational work needed for calculating the response data to establish the dynamic model of damaged structures is an important issue for dynamic damage detection. In this paper through using the advanced modeling method of element stiffness matrix modification, the order of the global stiffness matrix can be kept invariable in establishing the model of intact and damaged structures. Then, eigenvalue perturbation theory is introduced to obtain the eigenvalues and eigenvectors of the damaged structure for reducing the computation load. Two artificial neural networks (ANN) are trained based on the response data simulated using finite element method (FEM) and perturbation theory enhanced finite element method (PFEM), respectively. The damage identification capability of these two ANN's are compared. Results show that the PFEM using the first order eigenvalue perturbation theory provides enough precision for detecting small structural damage and the computational requirement is greatly reduced. Typically, the eigensolution computational time for obtaining the train sample data using PFEM is only 1% of that using the traditional FEM.
机译：经常基于一些实测数据和大量仿真数据来实施当前的结构损伤识别方法，例如遗传算法和人工神经网络。计算响应数据以建立受损结构的动力学模型所需的大量耗时的计算工作是动态损伤检测的重要问题。本文通过使用单元刚度矩阵修正的高级建模方法，可以在建立完好的受损结构模型时使整体刚度矩阵的阶数保持不变。然后，引入特征值微扰理论来获得损伤结构的特征值和特征向量，以减少计算量。基于分别使用有限元方法（FEM）和扰动理论增强的有限元方法（PFEM）模拟的响应数据，训练了两个人工神经网络（ANN）。比较了这两个人工神经网络的损伤识别能力。结果表明，采用一阶特征值摄动理论的PFEM为检测微小的结构损伤提供了足够的精度，大大降低了计算量。通常，使用PFEM获得火车样本数据的本征解计算时间仅为使用传统FEM的本征求解时间的1％。

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  • 作者

    Yu L; Cheng L; Yam LH; Yan YJ;

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  • 年度 2007
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  • 原文格式 PDF
  • 正文语种 eng
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