Chen, L., Hassan, H., Tallman, T.N. et al. (2 more authors) (2022) Predicting strain and stress fields in self-sensing nanocomposites using deep learned electrical tomography. Smart Materials and Structures, 31 (4). 045024. ISSN 0964-1726
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IOP Publishing Ltd. This Accepted Manuscript is available for reuse under a CC BY-NC-ND 3.0 licence (https://creativecommons.org/licences/by-nc-nd/3.0). |
Keywords: | Deep learning; electrical resistance tomography; nanocomposites; piezoresistivity |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Civil and Structural Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 03 Mar 2022 10:22 |
Last Modified: | 24 Feb 2023 01:13 |
Status: | Published |
Publisher: | IOP Publishing |
Refereed: | Yes |
Identification Number: | 10.1088/1361-665x/ac585f |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184292 |