Abstract
In this study, we present a data processing framework to apply measurements of the Global Navigation Satellite System (GNSS) technique for analyzing and predicting the movements of civil structures such as bridges. The proposed approach reduces the noise level of GNSS measurements using the Kalman Filter (KF) approach and enables the estimation of static, semi-static, and dynamic components of the bridge’s movements using a series of analyses such as the temporal filtering and the Least Squares Harmonic Estimation (LS-HE). The numerical results indicate that by using a RTK-GNSS system the semi-static component is extracted with a Standard Deviation (STD) of 0.032, 0.048, and 0.06 m in the North, East, and Up (NEU) directions, while that of the dynamic component is 0.004, 0.003, and 0.01 m, respectively. Comparing the dominant frequencies of the bridge movements from LS-HE with those of the permanent stations provides information about the bridge’s stability. To predict its deflection, the Neural Network (NN) technique is tested to simulate the time-varying components, which are then compared with the safety limits, known by its design, to assess the structural health under usual load.
Highlights
Nowadays, civil structures, such as bridges, have been growing ever faster to support, e.g., economical activities and attracting tourists
The present study evaluates the movement of a bridge using short-period monitoring by applying the RTK-Global Positioning System (GPS) measurement technique
We introduce a collection of signal processing techniques, which can be used for evaluating bridge movements
Summary
Civil structures, such as bridges, have been growing ever faster to support, e.g., economical activities and attracting tourists. The RTK system contains errors and noise of various statistical distribution (i.e., colored noise and white noise), which needs to be filtered before the precise displacement/deformation monitoring [14] This is achieved in previous works by implementing time series analysis techniques that enable the extraction of semi-static and dynamic components. Network (NN) method, Extreme Learning Machine (ELM), and Ant Colony Optimization algorithm (ACO)) [18,25,26,27,28,29,30] have been applied to predict the structural behaviors These studies demonstrated that the monitoring data of huge structures (e.g., dam, bridge, and tower) contain nonlinear characteristics due to the uncertain environment [29]. We introduce an efficient combination of time series analysis and prediction techniques to model movements of a bridge, which is monitored by the RTK-GNSS technique.
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