University of Oulu

Gallet A, Rigby S, Tallman TN, Kong X, Hajirasouliha I, Liew A, Liu D, Chen L, Hauptmann A, Smyl D. 2022 Structural engineering from an inverse problems perspective. Proc. R. Soc. A478: 20210526.

Structural engineering from an inverse problems perspective

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Author: Gallet, A.1; Rigby, S.1; Tallman, T. N.2;
Organizations: 1Department of Civil and Structural Engineering, University of Sheffield, Sheffield, UK
2School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, USA
3Department of Physics and Engineering Science, Coastal Carolina University, Conway, SC, USA
4School of Physical Sciences, University of Science and Technology of China, Hefei, People’s Republic of China
5Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
6Department of Computer Science, University College London, London, UK
7Department of Civil, Coastal, and Environmental Engineering, University of South Alabama, Mobile, AL, USA
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
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Language: English
Published: The Royal Society, 2022
Publish Date: 2022-04-27


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.

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Series: Proceedings of the Royal Society. A, Mathematical, physical and engineering sciences
ISSN: 1364-5021
ISSN-E: 1471-2946
ISSN-L: 1364-5021
Volume: 478
Article number: 20210526
DOI: 10.1098/rspa.2021.0526
Type of Publication: A2 Review article in a scientific journal
Field of Science: 111 Mathematics
113 Computer and information sciences
212 Civil and construction engineering
214 Mechanical engineering
Funding: A.H. is funded by Academy of Finland project nos. 338408 and 336796 (Finnish Centre of Excellence in Inverse Modelling and Imaging, 2018–2025). D.S. was supported by Engineering and Physical Sciences Research Council project no. EP/V007025/1. D.L. was supported by the National Natural Science Foundation of China under grant no. 61871356. A.G. was partially supported through the Ramboll Foundation PhD programme. X.K. was partially supported by the new faculty start-up research fund from the Gupta College of Science at Coastal Carolina University.
Academy of Finland Grant Number: 338408
Detailed Information: 338408 (Academy of Finland Funding decision)
336796 (Academy of Finland Funding decision)
Copyright information: © 2022 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License, which permits unrestricted use, provided the original author and source are credited.