Application of time series analysis for bridge monitoring

Despite recent considerable advances in structural health monitoring (SHM) of civil infrastructure, converting large amounts of data from SHM systems into usable information and knowledge remains a great challenge. This paper addresses the problem through the analysis of time histories of static strain data recorded by an SHM system installed in a major bridge structure and operating continuously for a long time. The reported study formulates a vector seasonal autoregressive integrated moving average (ARIMA) model for the recorded strain signals. The coefficients of the ARIMA model are allowed to vary with time and are identified using an adaptive Kalman filter. The proposed method has been used for analysis of the signals recorded during the construction and service life of the bridge. By observing various changes in the ARIMA model coefficients, unusual events as well as structural change or damage sustained by the structure can be revealed.