Identification Model for Dam Behavior Based on Wavelet Network
Identification Model for Dam Behavior Based on Wavelet Network
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DOI：
10.1111/j.1467-8667.2007.00499.x
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发表时间：
2007-08
期刊：
影响因子：
--
通讯作者：
H. Su;Zhong Wu;Z. Wen
中科院分区：
文献类型：
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作者：
H. Su;Zhong Wu;Z. Wen
翻译后摘要：大坝的行为是传统的评估与识别模型的变形，渗流，应力和裂缝开口。辨识模型需要用复杂的非线性函数来描述。首次将基于小波框架的小波网络应用于大坝性态识别。首先对训练数据进行时频分析，确定小波网络的原始结构;其次，根据网络输出与隐层节点之间的依赖关系，提出了一种新的迭代消除冗余神经元的方法。在该方法中，粗糙集理论被用来计算依赖。最后，利用训练好的小波网络建立了某混凝土拱坝位移和裂缝的识别模型。该模型能够反映荷载与坝体性态之间的关系。数值算例表明，所建模型合理，信号去噪效果显著。
Abstract: Dam behavior is conventionally evaluated with identification models of deformation, seepage, stress, and crack opening. The identification model needs to be described with a complicated and nonlinear function. Wavelet networks based on wavelet frames were used to establish the identification models of dam behavior for the first time. Firstly, time‐frequency analysis for training data was implemented to determine the original structure of the wavelet network. Next, a new method was proposed for iterative elimination of the redundant neurons according to the dependency between the network output and the nodes in the hidden layer. In this method, rough sets theory was used to calculate the dependency. Lastly, this article built the identification models for the displacement and cracks of one concrete arch‐dam with the trained wavelet network. The models can represent the connection between loads and the behavior of the dam. The numerical example shows that the proposed models are reasonable, and the denoising effect of the signal is remarkable.