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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.


The elevation view of Fumin Bridge and the test point arrangement.
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sensors-17-00436-f006: The elevation view of Fumin Bridge and the test point arrangement.

Mentions: The Tianjin Fumin Bridge is in a central urban area. The main bridge is a self-anchored suspension bridge with a single tower and spatial cables. The tower is supported by one column. The main span cable is anchored at two sides of the girder, and the side span cable is anchored onto the ground, forming a stable structural system. The main span cable uses a 3D spatial line, which is a parabola on the vertical and horizontal surfaces. The side span cables use a group (two parallel lines) of cables without vertical sling (Figure 6). The total length of the bridge is 340.6 m, including 157.081 m main span and 86.4 m side span. The approach bridge at the east of the river is (19 + 20 + 19.6) m (three-span ordinary reinforced concrete continuous beams), and the approach bridge at the west of the river (single-span concrete framework with cantilevers) is 38.219 m.


Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements
The elevation view of Fumin Bridge and the test point arrangement.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

sensors-17-00436-f006: The elevation view of Fumin Bridge and the test point arrangement.
Mentions: The Tianjin Fumin Bridge is in a central urban area. The main bridge is a self-anchored suspension bridge with a single tower and spatial cables. The tower is supported by one column. The main span cable is anchored at two sides of the girder, and the side span cable is anchored onto the ground, forming a stable structural system. The main span cable uses a 3D spatial line, which is a parabola on the vertical and horizontal surfaces. The side span cables use a group (two parallel lines) of cables without vertical sling (Figure 6). The total length of the bridge is 340.6 m, including 157.081 m main span and 86.4 m side span. The approach bridge at the east of the river is (19 + 20 + 19.6) m (three-span ordinary reinforced concrete continuous beams), and the approach bridge at the west of the river (single-span concrete framework with cantilevers) is 38.219 m.

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.