Progresses and Perspectives of Damage Identification in Bridge Structure

Article Preview

Abstract:

Structural damage identification is the core of Structural health monitoring system. The paper points out existing damage identification methods and analysis the application status and disadvantages of various methods. The research hotspots are summarized on considering the statistical identification method under uncertainty and the environmental impact of damage identification methods. Aiming at the characters of bridge engineering, the paper points out that a four-in-one multi-system damage identification method is the develop direction for bridge structural damage identification, which is established based on construction, load testing, health monitoring and manual inspection.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1632-1636

Citation:

Online since:

September 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Sohn H, Farrar C R, Hemez F M, et al. A review of structural health monitoring literature : 1996 – 2001[R]. Los Alomos National Laboratory, (2001).

Google Scholar

[2] REN Weixin, HAN Jiangang, SUN Zengshou. application of wavelet analytical method to civil engineering structures[M]. Beijing: (2006).

Google Scholar

[3] DING Youliang, LI Aiqun, GENG Fangfang, Monitoring and warning of health conditions for suspension bridges under varying environment conditions[J]. Journal of Southeast University(Natural Science Edition). 2010(5): 1052-1056.

Google Scholar

[4] ZONG Zhouhong, REN Weixin. Finite element model updating and model validation of bridge structures[M]. Beijing: China Communication Press, (2012).

Google Scholar

[5] SHAN Deshan, LI Qiao, FU Chunyu, et al. Smart bridge health monitoring and damage diagnosis[M]. Beijing: China Communication Press, (2010).

Google Scholar

[6] Collins J D, Kennedy B, Hart G C. Statistical identification of structures[J]. Aiaa Journal. 1973, 12(2): 185-190.

Google Scholar

[7] Beck J L. Statistical system identification of structures[C]. New York: ASCE, (1989).

Google Scholar

[8] Katafygiotis L S, Yuen K V, Chen J C. Bayesian modal updating by use of ambient data[J]. Aiaa Journal. 2001, 39(2): 271-278.

DOI: 10.2514/3.14727

Google Scholar

[9] Papadopoulos L, Garcia E. Structural damage identification: a probabilistic approach[J]. Aiaa Journal. 1998, 36(11): 2137-2145.

DOI: 10.2514/2.318

Google Scholar

[10] ZONG Zhouhong, NIU Jie, WANG Hao. A review of structural identification methods based on the finite element model validation[J]. China civil engineering journal. 2012(8): 121-130.

Google Scholar

[11] Sohn H, Farrar C R. Damage diagnosis using time series analysis of vibration signals[J]. Smart Materials and Structures. 2001, 10(3): 446.

DOI: 10.1088/0964-1726/10/3/304

Google Scholar

[12] ZHANG Qiwei. Damage feature extraction and novelty detection for bridge health monitoring[J]. Journal of Tongji University(Natural Science Edition). 2003(3): 258-262.

Google Scholar

[13] Bishop C M. Neural networks for pattern recognition[M]. Oxford: Oxford Univ. Press, (1995).

Google Scholar

[14] FENG Xin, ZHOU Jing, CHEN Jianyun. A two-stage method for identification of structural parameter[J]. Chinese Journal of Computational Mechanics. 2002, 19(2): 222-227.

Google Scholar

[15] 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.

DOI: 10.1016/j.engstruct.2005.02.020

Google Scholar

[16] Dyke S J, Caicedo J M, Johnson E A. Monitoring of a benchmark structure for damage identification[C]. (2000).

Google Scholar

[17] FENG Xin. Studies on structural identification method in civil engineering[D]. (2002).

Google Scholar

[18] Caicedo J M, Dyke S J, Johnson E A. Health monitoring based on component transfer functions[C]. (2000).

Google Scholar

[19] Nakamura M, Masri S F, Chassiakos A G, et al. A neural network approach to damage detection in a building from ambient vibration measurements[J]. Proceedings of the Spie-the International Society for Optical Engineering. 1998, 3321.

DOI: 10.1117/12.305542

Google Scholar

[20] Manson G. Identifying damage sensitive, environment insensitive features for damage detection[C]. (2002).

Google Scholar