Gallet, A. orcid.org/0000-0003-4939-9916, Rigby, S. orcid.org/0000-0001-6844-3797, Tallman, T.N. et al. (7 more authors) (2022) Structural engineering from an inverse problems perspective. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 478 (2257). 20210526. ISSN 1364-5021
Abstract
The field of structural engineering is vast, spanning areas from the design of new infrastructure to the assessment of existing infrastructure. From the onset, traditional entry-level university courses teach students to analyse structural responses given data including external forces, geometry, member sizes, restraint, etc.—characterizing a forward problem (structural causalities → structural response). Shortly thereafter, junior engineers are introduced to structural design where they aim to, for example, select an appropriate structural form for members based on design criteria, which is the inverse of what they previously learned. Similar inverse realizations also hold true in structural health monitoring and a number of structural engineering sub-fields (response → structural causalities). In this light, we aim to demonstrate that many structural engineering sub-fields may be fundamentally or partially viewed as inverse problems and thus benefit via the rich and established methodologies from the inverse problems community. To this end, we conclude that the future of inverse problems in structural engineering is inexorably linked to engineering education and machine learning developments.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
Keywords: | inverse problems; machine learning; non-destructive testing and evaluation; smart materials and structures; structural design; structural engineering |
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) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/V007025/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Feb 2022 09:09 |
Last Modified: | 12 Jul 2024 10:57 |
Status: | Published |
Publisher: | The Royal Society |
Refereed: | Yes |
Identification Number: | 10.1098/rspa.2021.0526 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183939 |
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