[1] Ding Youliang, Li Aiqun, Geng Fangfang,. Damage warning of suspension bridgesbased on neural networks under changing temperature conditions [J]. Journal of Southeast University (English Edition), 2010, 26 (4): 586-590. [doi:10.3969/j.issn.1003-7985.2010.04.018]

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Damage warning of suspension bridgesbased on neural networks under changing temperature conditions()

温度变化影响下基于神经网络的悬索桥损伤预警方法

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

26

Issue:

2010 4

Page:

586-590

Research Field:

Civil Engineering

Publishing date:

2010-12-30

Info

Title:

Damage warning of suspension bridgesbased on neural networks under changing temperature conditions

温度变化影响下基于神经网络的悬索桥损伤预警方法

Author(s):

Ding Youliang¹;  Li Aiqun¹;  Geng Fangfang²

¹ Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China
² Chengxian College, Southeast University, Nanjing 210096, China

丁幼亮¹;  李爱群¹;  耿方方²

¹东南大学混凝土及预应力混凝土结构教育部重点实验室, 南京210096; ²东南大学成贤学院, 南京210096

Keywords:

structural damage detection;  modal frequency;  temperature;  neural network;  suspension bridge

结构损伤预警;  模态频率;  温度;  神经网络;  悬索桥

PACS:

TU311.41

DOI:

10.3969/j.issn.1003-7985.2010.04.018

Abstract:

This paper aims at successive structural damage detection of long-span bridges under changing temperature conditions. First, the frequency-temperature correlation models of bridges are formulated by means of artificial neural network techniques to eliminate the temperature effects on the measured modal frequencies. Then, the measured modal frequencies under various temperatures are normalized to a reference temperature, based on which the auto-associative network is trained to monitor signal damage occurrences by means of neural-network-based novelty detection techniques. The effectiveness of the proposed approach is examined in the Runyang Suspension Bridge using 236-day health monitoring data. The results reveal that the seasonal change of environmental temperature accounts for variations in the measured modal frequencies with averaged variances of 2.0%. And the approach exhibits good capability for detecting the damage-induced 0.1% variance of modal frequencies and it is suitable for online condition monitoring of suspension bridges.

提出了考虑温度变化影响的悬索桥结构损伤预警方法.首先, 采用神经网络技术建立桥梁实测模态频率与温度的相关性模型, 用以消除温度变化对模态频率的影响.然后, 将不同温度下的实测模态频率进行“温度归一化”, 在此基础上利用神经网络新奇检测技术建立自联想神经网络进一步识别模态频率的异常变化.通过润扬大桥悬索桥236d的实测数据分析验证了该方法的可行性.分析结果表明, 不同季节下模态频率的相对变化平均约为2.0%, 采用所提方法可以识别出悬索桥模态频率0.1%的异常变化, 适用于悬索桥结构的在线整体状态监测.

References:

[1] Doebling S W, Farrar C R, Prim M B. A summary review of vibration-based damage identification methods [J]. Shock and Vibration Digest, 1998, 30(2): 91-105.
[2] Hsieh K H, Halling M W, Barr P J. Overview of vibrational structural health monitoring with representative case studies [J]. Journal of Bridge Engineering, 2006, 11(6): 707-715.
[3] Ni Y Q, Hua X G, Fan K Q, et al. Correlating modal properties with temperature using long-term monitoring data and support vector machine technique [J]. Engineering Structures, 2005, 27(12): 1762-1773.
[4] Abdel Wahab M, De Roeck G. Effect of temperature on dynamic system parameters of a highway bridge [J]. Structural Engineering International, 1997, 7(4): 266-270.
[5] Cornwell P, Farrar C R, Doebling S W. Environmental variability of modal properties [J]. Experimental Techniques, 1999, 23(6): 45-48.
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[8] Hua X G, Ni Y Q, Ko J M, et al. Modeling of temperature-frequency correlation using combined principal component analysis and support vector regression technique [J]. Journal of Computing in Civil Engineering, 2007, 21(2): 122-135.
[9] Ding Y L, Li A Q. Structural health monitoring of long-span suspension bridges using wavelet packet analysis [J]. Earthquake Engineering and Engineering Vibration, 2007, 6(3): 289-294.
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Memo

Memo:

Biographies: Ding Youliang(1979—), male, doctor, associate professor, civilding@163.com; Li Aiqun(1962—), male, doctor, professor, aiqunli@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.50725828, 50808041), the Natural Science Foundation of Jiangsu Province(No.BK2008312), the Ph.D. Programs Foundation of Ministry of Education of China(No.200802861011).
Citation: Ding Youliang, Li Aiqun, Geng Fangfang. Damage warning of suspension bridges based on neural networks under changing temperature conditions[J].Journal of Southeast University(English Edition), 2010, 26(4):586-590.

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