Damage Identification of Simply Supported Beam Bridge Based on Time Series Analysis

Abstract:

Presently, the study on damage identification of bridges is very popular and it has a wide range of applications. Also the related theory and technology are constantly developing and mature. The researches based on the dynamic response of bridge in frequency domain is more, but the dynamics theory is complex and difficult for the engineering personnel to grasp. On the opposite, although the damage identification method based on the dynamic response of bridge in time domain is easy to understand, it is difficulty for applications. The Auto Regressive Moving Average model (ARMA) of time series analysis can be used to settle this problem. It is a not very abstruse theory and it is already apply for the system identification of some Structures. In this paper, we use time series analysis for the damage identification of simply supported beam bridge combined with its own dynamic response in time domain.

• Periodical: Applied Mechanics and Materials (Volumes 236-237)
• Pages: 617-621
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.236-237.617
• Online since: November 2012
• Authors: Han Bing Liu, Yan Jun Song, Guo Jin Tan, Yan Yi Sun
• Keywords: ARMA Model, Damage Identification, Simply Supported Beam Bridge, Time Domain, Time Series Analysis

References

[1] Wang Liangbo. Analysis on Mechanical Characteristics and Bearing Capacity of Assembly Skew Hollow Slab Bridge. (2004).

[2] Ali R. Khaloo, H. Mirzabozorg, in: Load distribution factors in simply supported skew bridges. Bridge. Eng, ASCE No. 6, PP. 241, 2003. 5.

DOI: 10.1061/(asce)1084-0702(2003)8:4(241)

[3] Q. Lu, G. Ren, Y. Zhao, Multiple damage location with flexibility curvature and relative frequency change for beam structures[J]. Journal of Sound and vibration, 253(2002) 1101-1114.

DOI: 10.1006/jsvi.2001.4092

[4] E. P. Carden, P. Fanning, Vibration based condition monitoring: a review[J]. Structural Health Monitoring, 4(2004) 355-377.

DOI: 10.1177/1475921704047500

[5] J.J. Lee et al., Neural network-based damage detection for bridges considering errors in baseline finite elements models[J]. Journal of Sound and Vibration, 280(2005) 555–578.

DOI: 10.1016/j.jsv.2004.01.003

[6] P.Z. Qiao, K. Lu, W. Lestari, J. Wang, Curvature mode shape-based damage detection in composite laminated plates[J]. Journal of composite structures[J]. 80(2007) 409-428.

DOI: 10.1016/j.compstruct.2006.05.026

[7] Hauser M A, Kunst R M. Forecasting High-frequency Financial Data with the ARFIMA-ARCH Model [J]. Journal of Forecasting, 20(2001) 501-518.

DOI: 10.1002/for.803

[8] Li Songchen, Zhang Shiying. Co integration research of a general vector ARFIMA model based on independent components analysis[J], 5 (2007) 40-45.

[9] Housner G W, Bergman L A, Caughey T K, et al. Structural control: past, present and future [J]. Journal of Engineering Mechanics, 123(1997) 897-971.

[10] Brockwell P J, Davis R A. Introduction to time series and forecasting (second edition) [M]. New York: Springer-Ver-lag, (2002).

[11] Box G E P, Jenkins G M, Reinsel G C. Time series analysis: forecasting and control (the third edition) [M]. NY: Prentice-Hall Inc, (1994).