Application of time series analysis for bridge health monitoring

Continuously operating instrumented structural health monitoring (SHM) systems are becoming a practical alternative to replace visual inspection for assessment of condition and soundness of civil infrastructure, such as bridges. However, converting large amount of data from an SHM system into usable information is a great challenge to which special signal processing techniques must be applied. This study is devoted to modeling of time histories of static, hourly sampled strains recorded by an SHM system installed in a major bridge structure in Singapore and operating continuously for a long period of time. The reported efforts try to establish a seasonal autoregressive integrated moving average (ARIMA) model for the recorded strains. An important feature of the proposed ARIMA model is that its parameters are allowed to vary with time and are identified on-line using the Kalman filter approach. By observing various changes in the model parameters, unusual events as well as change or damage sustained by the structure can be revealed. Such events or structural changes may result, among other causes, from a sudden settlement of foundation, ground movement, excessive load or failure of post-tensioning cables. The proposed method has been applied to analysis of strains recorded during construction of the bridge.