  Within a structural health monitoring (SHM) framework, we propose a
simulation-based classification strategy to move towards online damage
localization. The procedure combines parametric Model Order Reduction (MOR)
techniques and Fully Convolutional Networks (FCNs) to analyze raw vibration
measurements recorded on the monitored structure. First, a dataset of possible
structural responses under varying operational conditions is built through a
physics-based model, allowing for a finite set of predefined damage scenarios.
Then, the dataset is used for the offline training of the FCN. Because of the
extremely large number of model evaluations required by the dataset
construction, MOR techniques are employed to reduce the computational burden.
The trained classifier is shown to be able to map unseen vibrational
recordings, e.g. collected on-the-fly from sensors placed on the structure, to
the actual damage state, thus providing information concerning the presence and
also the location of damage. The proposed strategy has been validated by means
of two case studies, concerning a 2D portal frame and a 3D portal frame railway
bridge; MOR techniques have allowed us to respectively speed up the analyses
about 30 and 420 times. For both the case studies, after training the
classifier has attained an accuracy greater than 85%.
