Chen, L., Gallet, A., Huang, S.-S. et al. (2 more authors) (2022) Probabilistic cracking prediction via deep learned electrical tomography. Structural Health Monitoring, 21 (4). pp. 1574-1589. ISSN 1475-9217
Abstract
In recent years, Electrical Tomography, namely Electrical Resistance Tomography (ERT), has emerged as a viable approach to detecting, localizing and reconstructing structural cracking patterns in concrete structures. High-fidelity ERT reconstructions, however, often require computationally-expensive optimization regimes and complex constraining and regularization schemes, which impedes pragmatic implementation in Structural Health Monitoring frameworks. To address this challenge, this paper proposes the use of predictive deep Neural Networks to directly and rapidly solve an analogous ERT inverse problem. Specifically, the use of cross-entropy loss is used in optimizing networks forming a nonlinear mapping from ERT voltage measurements to binary probabilistic spatial crack distributions (cracked/not cracked). In this effort, Artificial Neural Networks and Convolutional Neural Networks are first trained using simulated electrical data. Following, the feasibility of the predictive networks is tested and affirmed using experimental and simulated data considering flexural and shear cracking patterns observed from reinforced concrete elements.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
Keywords: | Artificial Intelligence; Deep learning; Electrical Resistance Tomography; Inverse Problems; Neural Networks; Structural Health Monitoring |
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: | 21 Jul 2021 09:55 |
Last Modified: | 23 Jun 2022 09:00 |
Status: | Published |
Publisher: | SAGE Publications |
Refereed: | Yes |
Identification Number: | 10.1177/14759217211037236 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176245 |