Dealing With the Failure Risk in Modal Tracking and Condition Evaluation From Damage of Sensors in Long‐Term Monitoring Using Adaptive Strategy

Modal tracking and condition evaluation are two important procedures in modal parameters based bridge condition evaluation, yet they always face the risk of failure due to the spatial aliasing caused by the damage of the sensors and the variation from the noise corruption. This paper proposed an automated modal tracking method and a variation removal method to get precise condition evaluation results. An index defined as modal similarity criterion (MSC) is first proposed to distinguish the modes when spatial aliasing occurs. Then, based on this index, a fully automated modal tracking method is established. An automated baseline mode setting strategy and an adaptive threshold updating strategy are proposed to make the modal tracking process fully automated and adapt to variation and spatial aliasing, even for closely spaced modes. The adaptive bandwidth kernel density estimation (ABKDE) is applied to the identified frequencies to reduce the variation from a statistical perspective to obtain more precise temperature–frequency regression and more precise condition evaluation results. The novelties and feasibilities of the proposed index and modal tracking method are validated through a theoretical analysis and real application to a benchmark bridge with closely spaced modes. The feasibility of the variation removal and condition evaluation method is validated through application to a suspension bridge with 15 years of continuous monitoring. The results indicate that the proposed index and modal tracking method are able to distinguish and automatically track the modes even when the mode shapes are not distinguishable (spatial aliasing occurs). The ABKDE is capable of reducing the variation in frequencies and helps to produce more precise regression and condition evaluation results.