Damage-Identification Method for Bridge Structures Based on Displacement Influence Line and Wavelet Packet Analysis
Publication: Journal of Performance of Constructed Facilities
Volume 37, Issue 6
Abstract
This paper proposes a method of identifying damage to bridge structures based on the principles of displacement influence line and wavelet packet transform. Three damage identification indicators were constructed, including the deviation of displacement influence line (DDIL), the deviation curvature of displacement influence line (DCDIL), and the relative energy rate of wavelet packet energy spectrum (RES). Taking a continuous beam bridge as an example, a finite element model is established to analyze the differences in using three indicators to locate the structural damage position, and to explore the effects of factors such as damage degree, damage location, dynamic response type, and bidirectional traffic on the identification effect. Next, the Zhengzhou Taohuayu Yellow River Bridge is taken as a practical case for verification analysis, and the anti-interference ability and sensitivity of the RES damage indicator are discussed under the interference of noise factors. The results demonstrate that the three identification indicators have excellent performance in identifying bridge structure damage. Compared to the DDIL, the DCDIL shows a more obvious mutation at the damage location, while the RES curve improves the problem of poor identification effect at locations far from the midspan of the bridge by requiring response information from multiple points of the bridge. The RES curve, which was constructed using displacement as the input parameter, exhibits the best comprehensive identification effect. Additionally, it maintains its robustness even when subjected to the interference of bidirectional traffic and noise.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
We would like to express our gratitude to the financial support provided by the National Natural Science Foundation of China (51408557), the China Postdoctoral Science Foundation (2013M541995), and the Program of the Department of Transportation of Henan Province (2020J-2-6), which has made this chapter possible.
References
He, H. X., W. Wang, and L. Huang. 2020. “Intelligent damage detection for bridge based on convolution neural network and recurrence plot.” [In Chinese.] J. Basic Eng. Sci. 28 (4): 966–980. https://10.16058/j.issn.1005-0930.2020.04.018.
Li, H. 2019. “Study on damage identification method of the frame structure based on wavelet packet energy.” Master’s thesis, School of Civil Engineering, Southwest Jiaotong Univ.
Liu, H., and Y. Zhang. 2020. “Bridge condition rating data modeling using deep learning algorithm.” Struct. Infrastruct. Eng. 16 (10): 1447–1460. https://doi.org/10.1080/15732479.2020.1712610.
Liu, L. J., and W. F. Liu. 2022. “Damage identification of bridge structures under unknown seismic excitation based on wavelet packet energy and transmissibility function.” [In Chinese.] J. Xiamen Univ. Nat. Sci. Ed. 61 (2): 308–313. https://doi.org/10.6043/j.issn.0438-0479.202012026.
Liu, X., X. K. Tao, and S. X. Zhang. 2019. “Simulation research on damage identification of simply supported beam based on wavelet packet energy method.” [In Chinese.] J. Syst. Simul. 31 (6): 1201–1207. https://doi.org/10.16182/j.issn1004731x.joss.17-0186.
Liu, Y. Q., X. D. Zhang, and J. L. Li. 2015. “Study of rock stress release rate in shallow-buried tunnel based on displacement-back-analysis.” [In Chinese.] J. Wuhan Univ. Technol. 39 (6): 1259–1262. https://doi.org/10.3963/j.issn.2095-3844.2015.06.037.
Long, F. 2020. Analysis and experimental study on the wind resistance performance of long span self-anchored suspension bridge, 121–130. Dalian, China: Dalian Univ. of Technology.
Prakash, G. 2021. “A deflection-based practicable method for health monitoring of in-service bridges.” Meas. Sci. Technol. 32 (7): 075108. https://doi.org/10.1088/1361-6501/abe287.
Wang, H. 2020. “Research on damage identification of bridge structure based on visual measurement and vibration theory.” Doctoral dissertation, School of Architecture and Engineering, Kunming Univ. of Science and Technology.
Yang, L. M. 2019. “Motion damage detection method based on wavelet analysis for sensor information fusion.” [In Chinese.] Sci. Technol. Eng. 19 (14): 262–267. https://doi.org/10.3969/j.issn.1671-1815.2019.14.039.
Zhao, Y. N., M. S. Gong, and Y. Yang. 2020. “A review of structural damage identification methods.” [In Chinese.] World Earthquake Eng. 36 (2): 73–84. https://doi.org/10.3969/j.issn.1007-6069.2020.02.008.
Zhou, Y., S. Di, C. Xiang, and L. Wang. 2018. “Damage detection for simply supported bridge with bending fuzzy stiffness consideration.” J. Shanghai Jiaotong Univ. 23 (Apr): 308–319. https://doi.org/10.1007/s12204-018-1939-4.
Information & Authors
Information
Published In
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Apr 13, 2023
Accepted: Jul 5, 2023
Published online: Aug 24, 2023
Published in print: Dec 1, 2023
Discussion open until: Jan 24, 2024
ASCE Technical Topics:
- Bridge engineering
- Bridges
- Bridges (by type)
- Continuous bridges
- Continuum mechanics
- Curvature
- Displacement (mechanics)
- Engineering fundamentals
- Engineering mechanics
- Finite element method
- Geometry
- Mathematical functions
- Mathematics
- Methodology (by type)
- Models (by type)
- Numerical methods
- Solid mechanics
- Structural analysis
- Structural engineering
- Structural mechanics
- Traffic models
- Wavelets
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.