## 4 Settlement Identification
### Interference Reduction  
The first two order natural frequencies of the transmission tower are verified to be effective references recorded by the surveillance sensor for settlement identification. The capacity of interference reduction in the Kriging surrogate model is measured by taking the uncertainties of the material property parameters into consideration. According to the real operation situation of transmission towers, the uncertainties and fluctuations in the material parameters, such as the Young's modulus, Poisson's ratio and physical density, are propagated in the finite element model. The signals received by the settlement surveillance sensor are interfered with by the factors corresponding to the changes and uncertainties in the material properties of transmission towers. The Kriging surrogate model is used to identify and filter the interference. The filtration percentage discussed in this study is the percentage of uncertain interference identified by the Kriging surrogate model for the transmission tower. The calculation of filtration percentage is based on the statistical mathematics. According to the two different order natural frequencies of the transmission tower, the prediction results of the settlement by the Kriging surrogate model form the sampling space for the interference identification. The predictive results of the Kriging surrogate model present the discrepancy or fluctuation if there are interferences and uncertainties in the system. The settlement discrepancy between two order natural frequencies is tracked and recorded for interference filtration. By the Kriging surrogate model proposed in this study, the filtration percentage results of interference reduction for the settlement surveillance sensor are presented in Figure 10. Each subfigure selects two order natural frequencies to jointly calculate the filtration percentage. For example, F1-F2 means the filtration percentage when taking the first and second order natural frequencies into consideration. Firstly, in Figure 10b-d, it can be found that the filtration percentages by the Kriging surrogate model are very stable and satisfied, and the values stay above 92% even if the accuracy of the Kriging surrogate model decreases. Thus, the first and the third order (F1-F3), the first and the fourth order (F1-F4) and the first and the fifth order (F1-F4) natural frequencies are the satisfied and robust signals used to identify and filter the interference in the settlement surveillance sensor In addition, the second and the third order (F2-F3), the second and the fourth order (F2-F4) and the second and the fifth order (F2-F4) natural frequencies, as shown in Figure 10e-g, can also reach as high as 92% of interference reduction, but with small performance degradation along with the accuracy decline of the Kriging surrogate model. The third and the fourth order (F3-F4) natural frequencies offered by the settlement surveillance sensor are not decent references to identify or judge the interference. Finally, according to the results in Figure 10a,i,j, with the accuracy reduction of the Kriging surrogate model, the filtration percentage results have evident deterioration in the settlement surveillance sensor. However, the Kriging surrogate model still provides 90% of filtration percentage in the cases of Figure 10a,i,j. Therefore, the Kriging surrogate model provides a satisfied and robust interference filtration capacity for the settlement surveillance sensor of the transmission tower. Furthermore, the Kriging surrogate model also has time-saving and high-efficiency merits in settlement surveillance sensors. The finite element model for natural frequency computation takes more time, is expensive and lacks the inverse judgment ability to identify the complicated interference. Figure 10: Filtration percentage results of interference reduction by the Kriging surrogate model for the settlement surveillance sensor (F1, F2, F3, F4 and F5 are the first five order natural frequencies, respectively).