  We investigate the capabilities of transfer learning in the area of
structural health monitoring. In particular, we are interested in damage
detection for concrete structures. Typical image datasets for such problems are
relatively small, calling for the transfer of learned representation from a
related large-scale dataset. Past efforts of damage detection using images have
mainly considered cross-domain transfer learning approaches using pre-trained
ImageNet models that are subsequently fine-tuned for the target task. However,
there are rising concerns about the generalizability of ImageNet
representations for specific target domains, such as for visual inspection and
medical imaging. We, therefore, propose a combination of in-domain and
cross-domain transfer learning strategies for damage detection in bridges. We
perform comprehensive comparisons to study the impact of cross-domain and
in-domain transfer, with various initialization strategies, using six publicly
available visual inspection datasets. The pre-trained models are also evaluated
for their ability to cope with the extremely low-data regime. We show that the
combination of cross-domain and in-domain transfer persistently shows superior
performance even with tiny datasets. Likewise, we also provide visual
explanations of predictive models to enable algorithmic transparency and
provide insights to experts about the intrinsic decision-logic of typically
black-box deep models.
