  Digital technologies can be used to gather accurate information about the
behavior of structural components for improving systems design, as well as for
enabling advanced Structural Health Monitoring strategies. New avenues for
achieving automated and continuous structural assessment are opened up via
development of virtualization approaches delivering so-called Digital Twins,
i.e., digital mirrored representations of physical. In this framework, the main
motivation of this work stems from the existing challenges in the
implementation and deployment of a real-time predictive framework for
virtualization of dynamic systems. Kalman-based filters are usually employed in
this context to address the task of joint input-state prediction in structural
dynamics. A Gaussian Process Latent Force Model (GPLFM) approach is exploited
in this work to construct flexible data-driven a priori models for the unknown
inputs, which are then coupled with a mechanistic model of the structural
component under study for input-state estimation. The use of GP regression for
this task overcomes the limitations of the conventional random-walk model, thus
limiting the necessity of offline user-dependent calibration of this type of
data assimilation methods. This paper proposes the use of alternative
covariance functions for GP regression in structural dynamics. A theoretical
analysis of the GPLFMs linked to the investigated covariance functions is
offered. The outcome of this study provides insights into the applicability of
each covariance type for GP-based input-state estimation. The proposed
framework is validated via an illustrative simulated example, namely a 3
Degrees of Freedom system subjected to an array of different loading scenarios.
Additionally, the performance of the method is experimentally assessed on the
task of joint input-state estimation during testing of a 3D-printed scaled wind
turbine blade.
