Title: Unknown input structural health monitoring
Description: The identification of a structural system deterministically or probabilistically is a topic of considerable interest and importance for its condition assessment and prediction. Many identification approaches, however, require the input which is not always available. Specifically, it may be impossible to know the input or, alternately, the measurement of the input is much more unreliable than the dynamic state measurement. Along these lines, engineers try to extract as much information as possible from the available output data to reduce the need for knowing the input. Three new methodologies are developed here to address this challenge. Initially, the input-parameter-state estimation capabilities of a novel unscented Kalman filter, for real time monitoring applications, is examined on both linear and nonlinear systems. The unknown input is estimated in two stages within each time step. Firstly, the predicted dynamic states and the system's parameters provide an estimation of the input.

Secondly, the corrected with measurements (updated) dynamic states and parameters provide a final input estimation for the current time step. Subsequently, the estimation of the dynamic states, the parameters, and the input of systems subjected to wind loading is examined using a sequential Kalman filter. The procedure considers two Kalman filters in order to estimate initially the dynamic states using kinematic constraints, and subsequently the system parameters along with the input, in an online fashion. Finally, the input-parameter-state estimation capabilities of a new residual-based Kalman filter are examined for both complete and limited output information conditions. The filter is based on the residual of the predicted and measured dynamic state output, as well as on the residual of the system model estimation. The considered sensitivity analysis is developed using a real time sensitivity matrix formulated by the filtered dynamic states.