Limits...
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.


GNSS-RTK elevation signal: (a) original signal-; and (b) signal after pretreatment-X1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5375722&req=5

sensors-17-00436-f008: GNSS-RTK elevation signal: (a) original signal-; and (b) signal after pretreatment-X1.

Mentions: Dynamic responses of the Fumin Bridge under ambient excitations were monitored for 10 successive hours from 10:00 a.m. to 8:00 p.m. on 9 November 2016. The GNSS sensor collected the vibration displacement signal of the structure, while the accelerometer collected the vibration acceleration signal of the structure. Four groups of data were collected. The effective number of satellite signals that has been used in the measurements is 14–16, and it was constant throughout the test. The GNSS sensor and accelerometer recorded original data at the sampling frequencies of 20 Hz and 100 Hz, respectively. The calculated results of RTK were transmitted and stored in laptops through USB cables. The original data were preprocessed by deleting abnormal values according to the principle of triple standard deviation (99.7% confidence interval) and repairing missed data by cubic spline interpolation and data smoothing by the moving average method, thus obtaining the signal after preprocessing (Xi). The original vertical displacement sequence (x1) and signal after preprocessing (X1) at the #1 measuring point are shown in Figure 8.


Operational Modal Analysis of Bridge Structures with Data from GNSS/Accelerometer Measurements
GNSS-RTK elevation signal: (a) original signal-; and (b) signal after pretreatment-X1.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

sensors-17-00436-f008: GNSS-RTK elevation signal: (a) original signal-; and (b) signal after pretreatment-X1.
Mentions: Dynamic responses of the Fumin Bridge under ambient excitations were monitored for 10 successive hours from 10:00 a.m. to 8:00 p.m. on 9 November 2016. The GNSS sensor collected the vibration displacement signal of the structure, while the accelerometer collected the vibration acceleration signal of the structure. Four groups of data were collected. The effective number of satellite signals that has been used in the measurements is 14–16, and it was constant throughout the test. The GNSS sensor and accelerometer recorded original data at the sampling frequencies of 20 Hz and 100 Hz, respectively. The calculated results of RTK were transmitted and stored in laptops through USB cables. The original data were preprocessed by deleting abnormal values according to the principle of triple standard deviation (99.7% confidence interval) and repairing missed data by cubic spline interpolation and data smoothing by the moving average method, thus obtaining the signal after preprocessing (Xi). The original vertical displacement sequence (x1) and signal after preprocessing (X1) at the #1 measuring point are shown in Figure 8.

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.