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

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.

Application of GeoSHM System in Monitoring Extreme Wind Events at the Forth Road Bridge

“…In the application of real-time bridge deformation monitoring, such low sampling rates restrict the capability of GNSS in detecting small dynamic responses at high vibrational frequencies, particularly for the case of short-span or medium-span bridges. The integration of a dual frequency GNSS receiver and a tri-axial accelerometer has been researched and realised as a viable solution to overcome this disadvantage [24][25][26]. The authors in Reference [24] used such a combination to measure sub-centimetric displacement and up to 5Hz modal frequencies of a short-span pedestrian bridge.…”

Dynamic characteristic of the forth road bridge estimated with GeoSHM

“…Moschas and Stiros (2011) also achieved the dynamic displacements and modal frequencies of a short-span pedestrian bridge using GPS and an/the accelerometer. Xiong et al (2017) proposed an AFEC mixed filtering algorithm to eliminate the multipath errors and random noise from GNSS and accelerometer data.…”

Global Navigation Satellite System‐based positioning technology for structural health monitoring: a review

“…The extracted multipath signals may be applied to enhance GNSS measurement accuracies. Xiong et al presented a filtering algorithm of combining autocorrelation function‐based EMD and Chebyshev filter to identify the dynamic displacements of bridges and an integrated method consisting of the expanded random decrement technique and autoregressive moving average model to identify natural frequencies of bridges. For the purpose of mitigating multipath in high‐precision GPS dynamic deformation monitoring, Dai et al developed two sidereal filtering approaches, that is, EMD‐ICA‐R and principle component analysis (PCA)‐EMD‐ICA‐R, where independent component analysis with a reference is combined with EMD and principle component analysis (PCA) to restructure the reference multipath.…”