  Continuous structural health monitoring (SHM) and integrated nondestructive
evaluation (NDE) are important for ensuring the safe operation of high-risk
engineering structures. Recently, piezoresistive nanocomposite materials have
received much attention for SHM and NDE. These materials are self-sensing
because their electrical conductivity changes in response to deformation and
damage. Combined with electrical impedance tomography (EIT), it is possible to
map deleterious effects. However, EIT suffers from important limitations -- it
is computationally expensive, provides indistinct information on damage shape,
and can miss multiple damages if they are close together. In this article we
apply a novel neural network approach to quantify damage metrics such as size,
number, and location from EIT data. This network is trained using a simulation
routine calibrated to experimental data for a piezoresistive carbon
nanofiber-modified epoxy. Our results show that the network can predict the
number of damages with 99.2% accuracy, quantify damage size with respect to the
averaged radius at an average of 2.46% error, and quantify damage position with
respect to the domain length at an average of 0.89% error. These results are an
important first step in translating the combination of self-sensing materials
and EIT to real-world SHM and NDE.
