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Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements

View Article: PubMed Central - PubMed

ABSTRACT

Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System (GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the close relationship between the GNSS multipath errors and measurement environment in combination with the noise reduction characteristics of different filtering algorithms, the researchers proposed an AFEC mixed filtering algorithm, which is an combination of autocorrelation function-based empirical mode decomposition (EMD) and Chebyshev mixed filtering to extract the real vibration displacement of the bridge structure after system error correction and filtering de-noising of signals collected by the GNSS. The proposed AFEC mixed filtering algorithm had high accuracy (1 mm) of real displacement at the elevation direction. Next, the traditional random decrement technique (used mainly for stationary random processes) was expanded to non-stationary random processes. Combining the expanded random decrement technique (RDT) and autoregressive moving average model (ARMA), the modal frequency of the bridge structural system was extracted using an expanded ARMA_RDT modal identification method, which was compared with the power spectrum analysis results of the acceleration signal and finite element analysis results. Identification results demonstrated that the proposed algorithm is applicable to analyze the dynamic displacement monitoring data of real bridge structures under ambient excitation and could identify the first five orders of the inherent frequencies of the structural system accurately. The identification error of the inherent frequency was smaller than 6%, indicating the high identification accuracy of the proposed algorithm. Furthermore, the GNSS dynamic deformation monitoring method can be used to monitor dynamic displacement and identify the modal parameters of bridge structures. The GNSS can monitor the working state of bridges effectively and accurately. Research results can provide references to evaluate the bearing capacity, safety performance, and durability of bridge structures during operation.

No MeSH data available.


Finite element modal analysis: (a) First order mode; (b) second-order mode; (c) third-order mode; (d) fourth-order mode; (e) five-order mode.
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sensors-17-00436-f017: Finite element modal analysis: (a) First order mode; (b) second-order mode; (c) third-order mode; (d) fourth-order mode; (e) five-order mode.

Mentions: Next, the power spectra of the de-noised displacement sequence and the acceleration signal sequence were analyzed (Figure 15 and Figure 16). The AFEC-filtered GNSS-RTK displacement sequence identified the frequency of the bridge structure (0.82 Hz), and the filtered acceleration signal sequence identified the first five orders of frequency of the bridge structure, valued at 0.84 Hz, 1.82 Hz, 2.59 Hz, 2.91 Hz, and 4.18 Hz, respectively. The identification results were close to the finite element analysis results (Figure 17). The MIDAS Civil 2012 was used to establish the 3-dimensional finite element theoretic calculation model for the Tianjin Fumin Bridge to calculate the self-vibration modal parameters of the structure. In the calculation and analysis model, cable and suspender are simulated by cable element. Moreover, girder and tower are simulated by beam element. The model contains 625 nodes, 62 cable units and 882 beam elements.


Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements
Finite element modal analysis: (a) First order mode; (b) second-order mode; (c) third-order mode; (d) fourth-order mode; (e) five-order mode.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5375722&req=5

sensors-17-00436-f017: Finite element modal analysis: (a) First order mode; (b) second-order mode; (c) third-order mode; (d) fourth-order mode; (e) five-order mode.
Mentions: Next, the power spectra of the de-noised displacement sequence and the acceleration signal sequence were analyzed (Figure 15 and Figure 16). The AFEC-filtered GNSS-RTK displacement sequence identified the frequency of the bridge structure (0.82 Hz), and the filtered acceleration signal sequence identified the first five orders of frequency of the bridge structure, valued at 0.84 Hz, 1.82 Hz, 2.59 Hz, 2.91 Hz, and 4.18 Hz, respectively. The identification results were close to the finite element analysis results (Figure 17). The MIDAS Civil 2012 was used to establish the 3-dimensional finite element theoretic calculation model for the Tianjin Fumin Bridge to calculate the self-vibration modal parameters of the structure. In the calculation and analysis model, cable and suspender are simulated by cable element. Moreover, girder and tower are simulated by beam element. The model contains 625 nodes, 62 cable units and 882 beam elements.

View Article: PubMed Central - PubMed

ABSTRACT

Real-time dynamic displacement and acceleration responses of the main span section of the Tianjin Fumin Bridge in China under ambient excitation were tested using a Global Navigation Satellite System (GNSS) dynamic deformation monitoring system and an acceleration sensor vibration test system. Considering the close relationship between the GNSS multipath errors and measurement environment in combination with the noise reduction characteristics of different filtering algorithms, the researchers proposed an AFEC mixed filtering algorithm, which is an combination of autocorrelation function-based empirical mode decomposition (EMD) and Chebyshev mixed filtering to extract the real vibration displacement of the bridge structure after system error correction and filtering de-noising of signals collected by the GNSS. The proposed AFEC mixed filtering algorithm had high accuracy (1 mm) of real displacement at the elevation direction. Next, the traditional random decrement technique (used mainly for stationary random processes) was expanded to non-stationary random processes. Combining the expanded random decrement technique (RDT) and autoregressive moving average model (ARMA), the modal frequency of the bridge structural system was extracted using an expanded ARMA_RDT modal identification method, which was compared with the power spectrum analysis results of the acceleration signal and finite element analysis results. Identification results demonstrated that the proposed algorithm is applicable to analyze the dynamic displacement monitoring data of real bridge structures under ambient excitation and could identify the first five orders of the inherent frequencies of the structural system accurately. The identification error of the inherent frequency was smaller than 6%, indicating the high identification accuracy of the proposed algorithm. Furthermore, the GNSS dynamic deformation monitoring method can be used to monitor dynamic displacement and identify the modal parameters of bridge structures. The GNSS can monitor the working state of bridges effectively and accurately. Research results can provide references to evaluate the bearing capacity, safety performance, and durability of bridge structures during operation.

No MeSH data available.