Enhancing static-load-test identification of bridges using dynamic data

In situ measurements have the potential to provide valuable information about the safety and the condition of bridges through implementation of system-identification methodology. A significant amount of research has focused on system identification using either dynamic or static measurements separately. Realizing the complementary relationship between static and dynamic measurements, traditional model updating methods adopt error functions to account for the residual between modeling and measured values for various types of measurements. Behavioral models may be inaccurate due to incomplete representation of modeling and measurement uncertainties. Furthermore, the normalization of error functions may bring additional uncertainty to the identification process. In this paper, an approach based on the model falsification method is proposed to combine both static and dynamic measurements with explicit consideration of both modeling and measurement uncertainties. A measurement selection strategy is also used to help detect abnormal measurements. The approach has been evaluated using a highway flyover bridge in Singapore. Dynamic measurement data include natural frequencies and mode shapes whereas static measurement data include inclinations, deflections and strains. By combining both static and dynamic measurements, this approach leads to falsification of additional model instances and obtains a more precise prediction of parameter values than approaches which interpret static measurements only.