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Journal Article

A multimodal natural frequency identification method of long-span bridges using GNSS


He,  Linyu
External Organizations;

Ju,  Boxiao
External Organizations;

Jiang,  Weiping
External Organizations;

Fan,  Wenlan
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Yuan,  Peng
0 Pre-GFZ, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Hu,  Junliang
External Organizations;

Chen,  Qusen
External Organizations;

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He, L., Ju, B., Jiang, W., Fan, W., Yuan, P., Hu, J., Chen, Q. (2023): A multimodal natural frequency identification method of long-span bridges using GNSS. - Measurement Science and Technology, 34, 10, 105122.

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5022685
Multimodal natural frequency is a crucial factor in determining the structural stability of bridges. Global navigation satellite system (GNSS) has become an increasingly important tool for monitoring the structural health of long-span bridges. This paper proposes a method for accurately determining multimodal natural frequencies in these structures using GNSS monitoring data. The proposed method involves decomposing GNSS displacement data into several signals that correspond to each mode using auto-regressive power spectrum decomposition, extraction of Intrinsic Mode Functions (IMFs) using empirical mode decomposition (EMD), identification of multimodal natural frequencies from the extracted IMFs using random decrement technique and Hilbert transform. The proposed method was validated through a simulation test and was applied to the Yingwuzhou Yangtze River Bridge. Results showed that this method was able to accurately identify the first six modal frequencies with a relative error of less than 8.09% compared to the theoretical values obtained through a finite-element model. This method outperforms other methods such as peak-picking, Complete Ensemble EMD with Adaptive Noise, and empirical wavelet transform, which can only identify the first three modes or fewer. Finally, four fieldwork experiments with different GNSS data show that the maximum range of relative errors of each identification is 3.65%, which fully demonstrates the effectiveness and universality of this method.