Direct identification of structural parameters from dynamic responses with neural networks
Direct identification of structural parameters from dynamic responses with neural networks
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DOI：
10.1016/j.engappai.2004.08.010
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发表时间：
2004-12
期刊：
影响因子：
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通讯作者：
Bin Xu;Zhishen Wu;Genda Chen;K. Yokoyama
中科院分区：
文献类型：
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作者：
Bin Xu;Zhishen Wu;Genda Chen;K. Yokoyama
提出并发展了一种新的基于神经网络的结构参数（刚度和阻尼系数）的直接识别策略，从目标结构的时域动力响应中识别结构参数，而无需任何特征值分析、提取和优化过程，这在许多反问题识别算法中是必需的。两个反向传播神经网络的构造，以促进参数识别的过程。第一种称为仿真器神经网络，用于对具有与待识别对象结构相同的总体尺寸和拓扑结构的参考结构的行为进行建模。在用参考结构在给定的动态激励下的动态响应进行适当的训练之后，仿真神经网络可以用作参考结构的非参数模型，以足够的精度预测其动态响应。然而，当参考结构的参数被修改以形成所谓的关联结构时，由网络预测的动力响应将不同于关联结构的模拟响应。它们的差异可以用建议的速度和位移响应的均方根（RMS）差向量来评估。利用相关的结构参数及其相应的RMS差向量，可以训练另一个网络，称为参数评估神经网络。在这项研究中，几个5层框架被认为是模拟位移和速度时程，模仿在实践中测得的动态响应的对象结构的例子。所提出的策略的性能已被证明是相当令人满意的，每个刚度或阻尼系数的估计误差小于10%，即使在存在7%的噪声。数值模拟表明，通过在参数评估神经网络的训练模式中注入噪声，可以显著提高参数识别的精度。所提出的策略是非常有效的计算，因此有可能成为一个实用的工具，近真实的时间监测的民用基础设施。
A novel neural network-based strategy is proposed and developed for the direct identification of structural parameters (stiffness and damping coefficients) from the time-domain dynamic responses of an object structure without any eigenvalue analysis and extraction and optimization process that is required in many identification algorithms for inverse problems. Two back-propagation neural networks are constructed to facilitate the process of parameter identifications. The first one, called emulator neural network, is to model the behavior of a reference structure that has the same overall dimension and topology as the object structure to be identified. After having been properly trained with the dynamic responses of the reference structure under a given dynamic excitation, the emulator neural network can be used as a nonparametric model of the reference structure to forecast its dynamic response with sufficient accuracy. However, when the parameters of the reference structure are modified to form a so-called associated structure, the dynamic responses forecast by the network will differ from the simulated responses of the associated structure. Their difference can be assessed with a proposed root mean square (RMS) difference vector for both velocity and displacement responses. With the associated structural parameters and their corresponding RMS difference vectors, another network, called parametric evaluation neural network, can be trained. In this study, several 5-story frames are considered as example object structures with simulated displacement and velocity time histories that mimic the measured dynamic responses in practice. The performance of the proposed strategy has been demonstrated quite satisfactorily; the error for the estimation of each stiffness or damping coefficient is less than 10% even in the presence of 7% noise. Numerical simulations show that the accuracy of the identified parameters can be significantly improved by injecting noise in the training patterns for the parametric evaluation neural network. The proposed strategy is extremely efficient in computation and thus has potential of becoming a practical tool for near real time monitoring of civil infrastructures.