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On the Effect of Electrical Impedance Tomography Error and Regularization Norms for Damage Identification in Piezoresistive Composites



Electrical impedance tomography (EIT) is a promising tool for the structural health monitoring (SHM) of composites that have been modified to be piezoresistive by the addition of carbon-based nanofillers. However, most studies have formulated the EIT problem to minimize an error vector in the least-squares sense while simultaneously using a least-squares term for regularization. This approach has important limitations in the context of SHM such as being extremely sensitive to outlier data due to damaged or faulty electrodes. Utilizing a least-squares term for regularization also makes EIT unable to image discontinuous conductivity losses such as those induced by fracture events. More sophisticated techniques that surmount these limitations have been studied in medical and mathematical venues, but these methods have not been thoroughly explored for piezoresistive imaging in SHM. Therefore, this article explores the effect of different error minimization and regularization norms on the ability of EIT to image impact damage in a carbon black (CB)-modified glass fiber/epoxy laminate.


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