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Continuous structural health monitoring (SHM) and integrated nondestructiveevaluation (NDE) are important for ensuring the safe operation of high-riskengineering structures. Recently, piezoresistive nanocomposite materials havereceived much attention for SHM and NDE. These materials are self-sensingbecause their electrical conductivity changes in response to deformation anddamage. Combined with electrical impedance tomography (EIT), it is possible tomap deleterious effects. However, EIT suffers from important limitations -- itis computationally expensive, provides indistinct information on damage shape,and can miss multiple damages if they are close together. In this article weapply a novel neural network approach to quantify damage metrics such as size,number, and location from EIT data. This network is trained using a simulationroutine calibrated to experimental data for a piezoresistive carbonnanofiber-modified epoxy. Our results show that the network can predict thenumber of damages with 99.2% accuracy, quantify damage size with respect to theaveraged radius at an average of 2.46% error, and quantify damage position withrespect to the domain length at an average of 0.89% error. These results are animportant first step in translating the combination of self-sensing materialsand EIT to real-world SHM and NDE.

Spatial Damage Characterization in Self-Sensing Materials via Neural Network-Aided Electrical Impedance Tomography: A Computational Study