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Research on Damage Identification of Cable-Stayed Bridges Based on Modal Fingerprint Data Fusion

Yue Cao1,2, Longsheng Bao1, Xiaowei Zhang1,*, Zhanfei Wang1, Bingqian Li1

1 School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang, 110168, China
2 College of Civil Engineering, Shenyang Jianzhu University, Shenyang, 110168, China

* Corresponding Author: Xiaowei Zhang. Email: email

Structural Durability & Health Monitoring 2024, 18(4), 485-503. https://doi.org/10.32604/sdhm.2024.049698

Abstract

This study addresses the issue of inaccurate single damage fingerprint recognition during the process of bridge damage identification. To improve accuracy, the proposed approach involves fusing displacement mode difference and curvature mode difference data for single damage identification, and curvature mode difference and displacement mode wavelet coefficient difference data for two damage identification. The methodology begins by establishing a finite element model of the cable-stayed bridge and obtaining the original damage fingerprints, displacement modes, curvature modes, and wavelet coefficient differences of displacement modes through modal analysis. A fusion program based on the D-S evidence theory criterion is then developed using Matlab to calculate the displacement mode difference (DMD), curvature mode difference (CMD), and wavelet coefficient difference of displacement mode (WCDM) for data fusion. The recognition effect of a single fingerprint is analyzed using modal data for both single and two damage conditions. Additionally, the fusion of multiple fingerprint initial data is performed, and the recognition effect of the data fusion method is quantitatively analyzed through sub peak to peak comparison. It is observed that when the fusion index is used as the recognition index, the sub peak to peak ratio is smaller compared to using the curvature mode as the damage index. Consequently, the reduction in single damage is more significant, ranging from 12% to 48%, while the reduction in multi damage is approximately 2%. This method demonstrates the ability to accurately identify the location of damage, yielding satisfactory recognition results and showing promising feasibility.

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Research on Damage Identification of Cable-Stayed Bridges Based on Modal Fingerprint Data Fusion

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APA Style
Cao, Y., Bao, L., Zhang, X., Wang, Z., Li, B. (2024). Research on damage identification of cable-stayed bridges based on modal fingerprint data fusion. Structural Durability & Health Monitoring, 18(4), 485-503. https://doi.org/10.32604/sdhm.2024.049698
Vancouver Style
Cao Y, Bao L, Zhang X, Wang Z, Li B. Research on damage identification of cable-stayed bridges based on modal fingerprint data fusion. Structural Durability Health Monit . 2024;18(4):485-503 https://doi.org/10.32604/sdhm.2024.049698
IEEE Style
Y. Cao, L. Bao, X. Zhang, Z. Wang, and B. Li "Research on Damage Identification of Cable-Stayed Bridges Based on Modal Fingerprint Data Fusion," Structural Durability Health Monit. , vol. 18, no. 4, pp. 485-503. 2024. https://doi.org/10.32604/sdhm.2024.049698



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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