  In the context of structural health monitoring (SHM), the selection and
extraction of damage-sensitive features from raw sensor recordings represent a
critical step towards solving the inverse problem underlying the structural
health identification. This work introduces a new way to enhance stochastic
approaches to SHM through the use of deep neural networks. A learnable feature
extractor and a feature-oriented surrogate model are synergistically exploited
to evaluate a likelihood function within a Markov chain Monte Carlo sampling
algorithm. The feature extractor undergoes a supervised pairwise training to
map sensor recordings onto a low-dimensional metric space, which encapsulates
the sensitivity to structural health parameters. The surrogate model maps the
structural health parameters onto their feature description. The procedure
enables the updating of beliefs about structural health parameters, effectively
replacing the need for a computationally expensive numerical (finite element)
model. A preliminary offline phase involves the generation of a labeled dataset
to train both the feature extractor and the surrogate model. Within a
simulation-based SHM framework, training vibration responses are
cost-effectively generated by means of a multi-fidelity surrogate modeling
strategy to approximate sensor recordings under varying damage and operational
conditions. The multi-fidelity surrogate exploits model order reduction and
artificial neural networks to speed up the data generation phase while ensuring
the damage-sensitivity of the approximated signals. The proposed strategy is
assessed through three synthetic case studies, demonstrating remarkable results
in terms of accuracy of the estimated quantities and computational efficiency.
