## 4 Method to Extract Foundation Settlement by Satellite Monitoring  
It is difficult to avoid deformation of the foundation of long-span bridge structure. Given this phenomenon, there exist many problems in the conventional method, precise leveling, such as large monitoring workload, error accumulation, long observation period, and so. Then, it is also difficult for this practice to achieve the purpose of rapid and accurate real-time monitoring of displacement, deformation, and settlement. Considering the above factors, GNSS satellite observation system can be regarded as a basis, and a monitoring system for settlement and deformation of bridge tower foundation is established, to realize continuous observation of foundation displacement in the stage of construction and operation. It is inevitable that there are acquisition errors in terms of the measured data, so the collected data should be denoised in real-time. In the construction stage of the bridge, the constructors observe the foundation displacement manually using the total station and other measuring devices. On this basis, the observation data obtained through GNSS satellite can be corrected according to the precise leveling data in the construction stage,  
Figure 4: Stability test of GNSS: (**a**) horizontal stability, (**b**) vertical stability. Figure 3: Comparison of the accuracy of precise leveling data and GNSS: (**a**) horizontal, (**b**) elevation. to provide the basis for the continuous observation of the foundation settlement and the prediction of the final settlement of the bridge in the operation stage. The error of satellite signals of monitoring the foundation displacement of bridge tower includes multipath errors and random noises. In this paper, SWT and improved EMD decomposition are respectively employed to eliminate the multipath error and random noise, to remove the error induced by GNSS signal collection to some extent. Considering the fact that the refraction and delay of the ionosphere and troposphere, as well as the multipath error cannot be absolutely eliminated, there are still errors between such denoised GNSS data and the foundation settlement obtained by precise leveling in the construction process. Generally, in the construction process, the measurement of the foundation displacement and the construction of the bridge structure are carried out simultaneously, and the process data such as the temporary load involved in the process and the quality of the bridge tower should be comprehensively mastered. As a consequence, based on the measurement data in the construction process, the data of foundation displacement obtained through GNSS signal can be corrected, and then the phased and final settlement can be predicted according to the revised data. At present, the method of using measured data to predict foundation settlement is generally to select a specific model, determine the parameters in the settlement model through parameter fitting, and then predict the phased or final settlement. The frequently used models for settlement prediction include Asaoka model [19], hyperbolic model [20], exponential model, Hoshino Nori model, Gompertz model, and Weibull model, among which the hyperbolic model, exponential model, and Hoshino Nori model are associated with two parameters, which can be expressed as  
\[S_{t}=S_{0}+\Delta S(a,b,t,t_{0}) \tag{5}\]  
where \(S_{t}\) is the total settlement with the time \(t\); \(S_{0}\) and \(\Delta S\) are the initial settlement and settlement, respectively; \(t_{0}\) and \(t\) are the initial time and elapsed time of settlement, respectively; \(a\) and \(b\) are coefficients. When the settlement model has been selected, the results of settlement observation are treated with parameter fitting based on the Equation (5) to represent the settlement of the foundation of bridge tower. The present work focuses on the development of a predictive tool to accurately estimate foundation settlement of the bridge tower.