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Component extraction method for GNSS displacement signals of long-span bridges

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Abstract

Global navigation satellite system (GNSS) receivers are commonly utilized for the displacement monitoring of long-span bridges. It is necessary for the structural early warning and identification to accurately extract the trend and dynamic displacement components from the original GNSS displacement signal. In this study, the solution for the component extraction is proposed by an improved variational mode decomposition (VMD) method, which is conducted by the recursive data-driven strategy (hereinafter referred to as RD-VMD). Further, the structural health monitoring (SHM) data of a long-span suspension bridge are used for validation. Firstly, the fourth-order Butterworth low-pass filter (BF) and ensemble empirical mode decomposition (EEMD) are compared to the proposed method for the trend extraction, and the comparison shows that the extraction error by RD-VMD is 1 cm smaller than those by the other two methods from the measured displacement with heavy vehicle passing. Then the measured modes during 168 h identified from the acceleration signals, the GNSS displacement signals, and the dynamic displacement sequences extracted by RD-VMD are compared. Consequently, the extracted dynamic displacement has the highest success rate of modal identification among the three data types. Especially for the second vertical mode, the effective identification results from the dynamic displacement sequences are at least 25% more than those from the other two data types. The statistical results show that the extracted dynamic displacement can be used for automated modal identification. Therefore, the feasibility of the proposed component extraction method is validated by the SHM data.

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Data Availability

Some or all data, models, or code generated or used during the study are available from the corresponding author by request.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (51978577) and the Science and Technology Project of Power China (No. SCMQ-201728-ZB).

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Authors and Affiliations

  1. School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Lingfeng Luo & Deshan Shan

  2. Sichuan Highway Planning, Survey, Design and Research Institute Ltd, Chengdu, 610041, China

    Erhua Zhang

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  1. Lingfeng Luo
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Correspondence to Deshan Shan.

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Luo, L., Shan, D. & Zhang, E. Component extraction method for GNSS displacement signals of long-span bridges. J Civil Struct Health Monit 13, 591–603 (2023). https://doi.org/10.1007/s13349-022-00661-6

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