Damage identification using physics-based datasets: From convolutional to metric-informed damage-sensitive feature extractors

Two alternative strategies addressing damage identification in structural health monitoring are presented in this contribution. Both strategies rely on reduced data representations – or features – to enable damage identification from vibrational data. To exploit a supervised learning scheme, training datasets are generated through numerical simulations, possibly speeded up through reduced order modelling. The first strategy deals with damage identification as a classification task employing onedimensional convolutional neural networks. Despite the good performance displayed in the proposed numerical benchmark of an eight-storey building, this approach suffers from the need of defining the possible damage classes a–priori, and from the lack of robustness of the extracted features. Both issues are successfully addressed by a second strategy, which relies on a Siamese architecture to learn a damage-sensitive low-dimensional metric space. In this second case, damage identification can be performed by solving a regression task in the learned metric space. This second approach is assessed against a test case involving a railway bridge, displaying impressive damage localization capabilities.