Input-state-parameter estimation of structural systems from limited output information. (1st July 2019) - Dertimanis, V.K.; Chatzi, E.N.; Eftekhar Azam, S.; Papadimitriou, C.
Highlights: A novel Bayesian observer that recombines the dual and unscented Kalman filters is proposed for addressing the joint input-state-parameter estimation problem. The dual observer is designed on realistic assumptions on instrumentation capacity and structural uncertainty. The stability and observability requirements of the novel scheme are studied and a nonlinear observability test is provided. An extensive parametric validation and assessment of the observer is offered proving efficiency. Abstract: A successive Bayesian filtering framework for addressing the joint input-state-parameter estimation problem is proposed in this study. Following the notion of analytical, rather than hardware redundancy, the envisaged scheme, (i) adopts realistic assumptions on the sensor network capacity; and (ii) allows for a certain degree of uncertainty in the structural information available throughout the life-cycle of the monitored structure. This uncertainty is quantitatively expressed via a parameter vector of known functional relationship to the structural matrices. An observer is accordingly established, which recombines the dual and unscented Kalman filters. The former aims at tackling the unknown structural excitations, while the latter solves the state and parameter estimation problem via an augmented state-space. An extensive parametric study on simulated structural systems under different measurement setups, excitation types and structural properties demonstrates the method's effectiveness.