Predicting strain and stress fields in self-sensing nanocomposites using deep learned electrical tomography

Abstract

Conductive nanocomposites, enabled by their piezoresistivity, have emerged as a new instrument in structural health monitoring. To this end, studies have recently found that electrical resistance tomography (ERT), a non-destructive conductivity imaging technique, can be utilized with piezoresistive nanocomposites to detect and localize damage. Furthermore, by incorporating complementary optimization protocols, the mechanical state of the nanocomposites can also be determined. In many cases, however, such approaches may be associated with high computational cost. To address this, we develop deep learned frameworks using neural networks to directly predict strain and stress distributions -- thereby bypassing the need to solve the ERT inverse problem or execute an optimization protocol to assess mechanical state. The feasibility of the learned frameworks is validated using simulated and experimental data considering a carbon nanofiber plate in tension. Results show that the learned frameworks are capable of directly and reliably predicting strain and stress distributions based on ERT voltage measurements.

Crack identification using electrical impedance tomography and transfer learning

“…However, due to the inherent differences between the FE model and the real body used in a field application (e.g., measurement noise, modeling, and fabrication errors), the real and virtual voltage measurements may differ substantially. In some examples, pre‐processing was performed on simulated data to limit this issue (L. Chen, Hassan, et al., 2022). Yet, this approach may be challenging to apply with difference imaging.…”

“…In some examples, pre-processing was performed on simulated data to limit this issue (L. Chen, Hassan, et al, 2022). Yet, this approach may be challenging to apply with difference imaging.…”

“…L. Chen, Hassan, et al. (2022) employed a deep learned framework to reconstruct the strain and stress fields in piezoresistive nanocomposites for damage identification, bypassing the resolution of the inverse EIT problem. In this case, the authors used an absolute imaging approach due to its suitability for reducing the discrepancy between simulated data (used for training) and real measurements.…”

“…Very recently, Jeon et al (2022) employed a deep learning-based approach to identify rebar position in reinforced concrete elements with high accuracy using a frequency-difference EIT approach. L. Chen, Hassan, et al (2022) employed a deep learned framework to reconstruct the strain and stress fields in piezoresistive nanocomposites for damage identification, bypassing the resolution of the inverse EIT problem. In this case, the authors used an absolute imaging approach due to its suitability for reducing the discrepancy between simulated data (used for training) and real measurements.…”

Implementation and evaluation of a soft tactile sensor using electrical impedance tomography

“…Among them, conductive polymer composites, consisting of conductive phase and non-conductive elastomeric phase, show benefits of easy fabrication and scalability for large-area sensing. Different conductive filler materials have been integrated with the EIT technique, including carbon nanotubes [11], carbon fiber [12], and metal particles [13]. However, the electrical response of the compliant sensors is highly related to its piezoresistive sensing mechanism, and the sensing performance varies accordingly [14].…”