Technology and Application of Structural Health Condition Assessment for Sutong Bridge （1）: Damage Identification of Stay Cable

杨杰; 李爱群; 丁幼亮; 姚蓓; 杨军; 张维苏

doi:10.13409/j.cnki.jdpme.2010.03.005

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Technology and Application of Structural Health Condition Assessment for Sutong Bridge （1）: Damage Identification of Stay Cable

更新时间：2025-12-11

- Technology and Application of Structural Health Condition Assessment for Sutong Bridge （1）: Damage Identification of Stay Cable
- Vol. 30, Issue 3, Pages: 325-329(2010)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.13409/j.cnki.jdpme.2010.03.005
  CLC： 441.4;U446
- Published：2010
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[1]杨杰,李爱群,丁幼亮,姚蓓,杨军,张维苏.苏通大桥结构健康状态评估技术研究与应用(1):拉索损伤识别[J].防灾减灾工程学报,2010,30(03):325-329.

杨杰, 李爱群, 丁幼亮, et al. Technology and Application of Structural Health Condition Assessment for Sutong Bridge （1）: Damage Identification of Stay Cable[J]. 2010, 30(3): 325-329.
[1]杨杰,李爱群,丁幼亮,姚蓓,杨军,张维苏.苏通大桥结构健康状态评估技术研究与应用(1):拉索损伤识别[J].防灾减灾工程学报,2010,30(03):325-329. DOI： 10.13409/j.cnki.jdpme.2010.03.005.

杨杰, 李爱群, 丁幼亮, et al. Technology and Application of Structural Health Condition Assessment for Sutong Bridge （1）: Damage Identification of Stay Cable[J]. 2010, 30(3): 325-329. DOI： 10.13409/j.cnki.jdpme.2010.03.005.

摘要

采用改进的RBF神经网络建立了苏通大桥拉索损伤识别方法。2个不同阶固有频率之比是仅与损伤位置有关的结构振动参数

据此定义了用于损伤定位的损伤特征指标

并用其来训练神经网络;提出了基于R2+准则与Jackknife校验的改进RBF算法

以有效地控制RBF网络的过拟合现象。算例结果表明

所提出的方法可以较好地对苏通大桥斜拉索进行损伤识别。

Abstract

In this paper the damage identification method for stay cable of Sutong Bridge is established using the modified RBF neural networks.The ratio of two different frequencies of the main girder is a vibration character which varies only with the damage location.It is selected as damage analysis index to train the neural networks.And in order to control the over-fitting of RBF neural networks

a new modified algorithm is presented based on R2+ rule and Jackknife validation.The analysis results show that the presented method can effectively locate the cable damage of Sutong Bridge.

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references

BP神经网络在大跨斜拉桥的斜拉索损伤识别中的应用 [J]. 杨杰,李爱群,缪长青. 土木工程学报 . 2006(05)

基于径向基函数神经网络的斜拉桥损伤识别 [J]. 张刚刚,王春生,徐岳. 长安大学学报(自然科学版) . 2006(01)

RBF神经网络及其在结构损伤识别中的应用研究 [J]. 饶文碧,吴代华. 固体力学学报 . 2002(04)

斜拉桥损伤识别的径向基函数（RBF）神经网络设计 [J]. 刘效尧. 工程设计CAD与智能建筑 . 2000(07)

Modal Analysis for Damage Detection in Structures [J] . George Hearn,Rene B. Testa. Journal of Structural Engineering . 1991 (10)

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