Substructural identification using neural networks
Substructural identification using neural networks
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DOI：
10.1016/s0045-7949(99)00199-6
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发表时间：
2000-08-01
影响因子：
4.7
通讯作者：
Bahng, EY
中科院分区：
文献类型：
--
作者：
Yun, CB;Bahng, EY
在现有结构的损伤检测和安全评估中，单元级刚度参数的估计是一个重要的问题。提出了一种利用反向传播神经网络估计复杂结构系统刚度参数的方法。采用几种技术来克服与大型结构系统中的许多未知参数相关联的问题。它们是子结构识别和子矩阵比例因子。的固有频率和振型被用作输入模式的神经网络的有效元素级识别，特别是对于不完整的测量的振型的情况下。拉丁超立方采样和组件模式合成方法是适用于有效的生成模式的训练神经网络。在学习过程中还采用了噪声注入技术，以减少测量误差对估计精度的影响。通过对桁架和框架结构的算例分析，验证了该方法的有效性。(C)2000 Elsevier Science Ltd.保留所有权利。
In relation to the problems of damage detection and safety assessment of existing structures, the estimation of the element-level stiffness parameters becomes an important issue. This study presents a method for estimating the stiffness parameters of a complex structural system by using a backpropagation neural network. Several techniques are employed to overcome the issues associated with many unknown parameters in a large structural system. They are the substructural identification and the submatrix scaling factor. The natural frequencies and mode shapes are used as input patterns to the neural network for effective element-level identification particularly for the case with incomplete measurements of the mode shapes. The Latin hypercube sampling and the component mode synthesis methods are adapted for efficient generation of the patterns for training the neural network. Noise injection technique is also employed during the learning process to reduce the deterioration of the estimation accuracy due to measurement errors, Two numerical example analyses on a truss and a frame structures are presented to demonstrate the effectiveness of the present method. (C) 2000 Elsevier Science Ltd. Ail rights reserved.