Estimation of static and dynamic behavior of engineering structures with time series analysis of GPS observations Case Study: Quds cable bridge of Ardabil

The behavior of engineering structures affected by dynamic and static loads are different depending on the type of design, the type of materials used in them and the operating time of the structures. These behaviors can be observed and evaluated with the time series obtained from GPS observations in the time and the frequency domains. The current study was conducted with the aim of investigating the behavior of Quds cable bridge of Ardabil, using the geodetic monitoring of the GPS system at a rate of 30 seconds. Microgeodesy network is used for static data collection and RTKLIB software with Kalman filter capability used for initial processing and time series formation. The processing results have an error of 3 mm. In order to evaluate the movement of the bridge, moving average, time average, and median filters have been used to estimate the semi-static, static, and dynamic components of the bridge movement, respectively. The results of applying the moving average filter indicate that the semi-static component is extracted with 4 mm accuracy in the East, 1 mm in the North and 2 mm in the Height directions. The RMSE between the obtained dynamic component and the short period component is 0.8, 0.1 and 4 mm in three directions E, N and H respectively. In order to extract the dominant frequencies of the bridge, the Fast Fourier Transform (FFT) method was used. Then, based on FFT, the periodogram diagram was drawn and the dominant frequencies were identified. In practice, numerical methods such as the periodogram diagram and its visual inspection are used to extract the existing dominant frequencies. A periodogram diagram presents the relation between the power spectrum and the frequency. In the next step, this diagram is used to compare the power spectrum of each frequency with its neighboring frequencies. Finally, the frequencies with a higher power spectrum will be considered as the dominant frequencies. Finally, the Artificial Neural Network model (ANN) with 10 hidden layers and 10 delays was used to predict bridge deformation and the accurate evaluation of the components. The results obtained from the processing of GPS observations in the time and frequency domains indicate the least changes compared to the safety design range of the bridge behavior.
Besides, the frequency analysis of the bridge movement time series and the Neural Network model, can be used to detect significant frequency changes and study the bridge performance rigidity, respectively.
Given that, since bridges are designed with a high reliability factor for static and dynamic loads more than the loads received during bridge monitoring, the natural frequencies of the bridge are extracted with the existing process with high accuracy, which can be the basis of early warnings. On the other hand, the bridge health monitoring system should be based on modern measurement methods. In the case of Quds Bridge, its complete behavioral investigation shows that the bridge is safe in its current condition and within the design limits.
In order to evaluate the frequency content other than the fast Fourier transform, it is also suggested to use short time Fourier transform and wavelet transforms.