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

TL;DR

This study introduces a neural network method to enhance electrical impedance tomography for damage detection in self-sensing nanocomposite materials, achieving high accuracy in identifying damage characteristics for structural health monitoring.

Contribution

The paper presents a novel neural network approach trained on simulated and experimental data to accurately quantify damage size, number, and location from EIT data in self-sensing materials.

Findings

Neural network predicts damage count with 99.2% accuracy.

Damage size is quantified with approximately 2.46% error.

Damage position is estimated with about 0.89% error.

Abstract

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.