Structural Health-Monitoring Strategy Based on Adaptive Kalman Filtering

Structures are exposed to aging and extreme events that can decrease the relevant safety margins or even lead to (partial) collapse mechanisms under unforeseen loading conditions. Structural health monitoring (SHM) therefore appears to be compulsory to avoid accidents by tracking the evolution of the state of the system and sending out warnings as soon as critical conditions are met or drifts from the response of the undamaged structure are identified. One of the approaches to online SHM rests on Kalman filtering, which is able to build the time evolution of the structural state upon the Bayes’ rule. In a customary joint version of the filtering procedure, state variables and health parameters are joined together in an extended state vector; while state variables, e.g., lateral displacements of shear buildings, can be observed thanks to pervasive sensor networks, the health parameters usually linked to the structural stiffness cannot, leading to possible divergence issues characterized by biases in the estimates. These issues are further enhanced by difficulties in setting the covariance terms, whose initialization is required to utilize Kalman filters. In this work, we investigate an adaptive strategy to the online tuning of the aforementioned covariance terms, leading to an improvement of the filter outcomes without issues related to its instability. This procedure is then applied to the SHM of a shear building, to highlight the excellent results in terms of accuracy and robustness.