| 基于静力位移及频率的结构损伤识别神经网络方法 |
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| 引用本文： | 袁旭东,周晶,黄梅.基于静力位移及频率的结构损伤识别神经网络方法[J].哈尔滨工业大学学报,2005,37(4):488-490. |
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| 作者姓名： | 袁旭东 周晶 黄梅 |
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| 作者单位： | 大连理工大学,土木水利学院,辽宁,大连,116024;辽宁工程技术大学,力学系,辽宁,阜新,123000 |
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| 基金项目： | 国家自然科学基金重点资助项目(5043901050378012) |
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| 摘 要： | 模型误差、量测噪声及量测数据不完整等因素是制约结构损伤识别技术应用的主要难点.为此,利用结构部分节点静力位移以及前几阶固有频率构造出神经网络合适的输入参数形式.采用改进动量BP神经网络算法对一五榀桁架结构进行了损伤识别数值模拟研究.识别结果表明,在一定水平噪声及量测数据不完备条件下,网络仍有较好的识别损伤位置及程度能力.
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| 关 键 词： | 静力位移 固有频率 神经网络 噪声 |
| 文章编号： | 0367-6234(2005)04-0488-03 |
| 修稿时间： | 2003年1月3日 |
A method of structural damage identification using neural networks based on static displacements and natural frequencies |
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| YUAN Xu-dong,ZHOU Jing,HUANG Mei.A method of structural damage identification using neural networks based on static displacements and natural frequencies[J].Journal of Harbin Institute of Technology,2005,37(4):488-490. |
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| Authors: | YUAN Xu-dong ZHOU Jing HUANG Mei |
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| Abstract: | Some factors, such as modeling error, measured noises, incomplete measured data, are main difficulties for many structural damage processes being utilized. The input parameter vectors for neural networks are constituted by using static displacements on partial nodes and several low frequencies. A damage numerical verification study on a five-bay truss was carried out by using an improved momentum BP neural network. 1-dentification results indicate that the neural networks have excellent capability to identify structural damage location and damage extent under the conditions of limited noises and incomplete measured data. |
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| Keywords: | static displacement natural frequency neural networks measured noises |
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