Multiscale Time Series Decomposition for Structural Dynamic Properties: Long-Term Trend and Ambient Interference

In structural health monitoring (SHM), variations of structural dynamic properties are paramount to indicate the health status of structures. Structural dynamic properties are time variant due to various long-term effects (e.g., structural deterioration) and periodic effects (e.g., periodic variations of temperature, humidity, and traffic). Sometimes periodic effects will interfere with the quantification of long-term effects. Though important, there are still limited research studies aiming to distinguish these two effects. Given the amount of SHM data, it is possible to solve the issue from a data-driven perspective. This article proposes a time series decomposition methodology to divide the time series of structural dynamic properties into long-term parts, multiscale periodic parts, holiday parts, and error parts. We extract the 10-year dynamic properties of a long-span bridge using the fast Bayesian FFT identification algorithm and choose two dynamic modes of that bridge as examples to explain how our proposed methodology works. We use the long-term parts to extract the rules of structural deterioration. The multiscale periodic parts are utilized to find the relationships with the periodically varying ambient conditions (i.e., temperature and humidity) in different time scales (i.e., yearly, weekly, and daily). Then, the long-term and periodic effects can be distinguished. For the long-term effects, the modal frequencies tend to decrease but the damping ratios seem to increase. For the periodic effects, we find that the increment of temperature will lead to the decrease of both modal frequencies and damping ratios. The variations of modal frequencies induced by deterioration and temperature are of the same amplitudes. The variations of both modal frequencies and damping ratios are not significantly related with humidity. This article could provide references for damage detection and safety assessment for similar bridges.