SHM methods for damage detection and localization in plate-like structures have typically relied on signal postprocessing techniques applied to ultrasonic guided-waves. The time of flight is one of these signals features which has been extensively used by the SHM community for damage localization. One approach for obtaining the time of flight is by applying a particular time-frequency transform to capture the frequency and energy content of the wave at each instant of time. To this end, the selection of a suitable methodology for time-frequency transform among the many candidates available in the literature has typically relied on experience, or simply based on considerations about computational efficiency. In this paper, a full probabilistic method based on the Bayesian inverse problem is proposed to rigorously provide a robust estimate of the time of flight for each sensor independently. Then, the robust prediction is introduced as an input to the Bayesian inverse problem of damage localization. The results reveal that the proposed methodology is able to efficiently reconstruct the damage localization within a metallic plate without the need to assume a specific a priori time-frequency transform model.

Condition-based maintenance critically relies on efficient and reliable structural health monitoring systems, where the number, position and type of sensors are determined according to rational and principled criteria. This paper proposes the use of the value of information and the relative expected information gain as optimality criteria to determine the best number and positions of sensors, respectively. The proposed methodology is general, but in this paper it is specialized for ultrasonic guided-wave optimal system configuration. Two case studies are used to illustrate the suitability of the proposed methodology in providing the optimal sensor configuration of an ultrasonic guided-wave based structural health monitoring system. The results confirm the value of information as an efficient and rational index to compare among different sensor positioning strategies, while accounting for the underlying modeling and measurement uncertainties. As key contribution, a novel framework that trades-off between amount and cost of information is provided. The results show that geometrically unconstrained sensor configurations are preferred, since they provide a healthier balance between the amount of information and the benefit of such information.

Ultrasonic Guided Waves (GW) actuated by piezoelectric transducers installed on structures have proven to be sensitive to small structural defects, with acquired scattering signatures being dependent on the damage type. This study presents a generic framework for probabilistic damage characterization within complex structures, based on physics-rich information on ultrasound wave interaction with existent damage. To this end, the probabilistic model of wave scattering properties estimated from measured GWs is inferred based on absolute complex-valued ratio statistics. Based on the probabilistic model, the likelihood function connecting the scattering properties predicted by a computational model containing the damage parametric description and the scattering estimates is formulated within a Bayesian system identification framework to account for measurement noise and modeling errors. The Transitional Monte Carlo Markov Chain (TMCMC) is finally employed to sample the posterior probability density function of the updated parameters. However, the solution of a Bayesian inference problem often requires repeated runs of "expensive-to-each "numerical experiment", the training outputs (i.e. ultrasound scattering properties) are efficiently computed using the hybrid WFE scheme which combines conventional FE analysis with periodic structure theory. By establishing the relationship between the training outputs and damage characterization parameters statistically, the surrogate model further enhances the computational efficiency of the exhibited scheme. Two case studies including one numerical example and an experimental one are presented to verify the accuracy and efficiency of the proposed algorithm.