Crack identification using electrical impedance tomography and transfer learning
نویسندگان
چکیده
Sensing skins and electrical impedance tomography constitute a convenient inexpensive alternative to dense sensor networks for distributed sensing in civil structures. However, their performance can deteriorate with the aging of film. Guaranteeing high identification after minor lesions is crucial improving ability identify structural damage. In this paper, resistance used crack locations nanocomposite paint sprayed onto components. The main novelty consists using annotations collected during visual inspections improve deep neural trained simulated datasets through transfer learning. Transfer component analysis employed simulation-to-real information applied at population level, extracting low-dimensional domain-invariant features shared by models structures similar geometry. results show that proposed method outperforms traditional approaches localization complex damage patterns.
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ژورنال
عنوان ژورنال: Computer-aided Civil and Infrastructure Engineering
سال: 2023
ISSN: ['1093-9687', '1467-8667']
DOI: https://doi.org/10.1111/mice.13043