Crack identification using electrical impedance tomography and transfer learning

Sensing skins and electrical impedance tomography constitute a convenient and inexpensive alternative to dense sensor networks for distributed sensing in civil structures. However, their performance can deteriorate with the aging of the sensing film. Guaranteeing high identification performance after minor lesions is crucial to improving their ability to identify structural damage. In this paper, electrical resistance tomography is used to identify the crack locations in nanocomposite paint sprayed onto structural components. The main novelty consists of using crack annotations collected during visual inspections to improve the crack identification performance of deep neural networks trained using simulated datasets through transfer learning. Transfer component analysis is employed for simulation-to-real information transfer and applied at a population level, extracting low-dimensional domain-invariant features shared by simulated models and structures with similar geometry. The results show that the proposed method outperforms traditional approaches for crack localization in complex damage patterns.