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Gallet A, Rigby S, Tallman TN, Kong X, Hajirasouliha I, Liew A, Liu D, Chen L, Hauptmann A, Smyl D. Structural engineering from an inverse problems perspective. Proc Math Phys Eng Sci 2022; 478:20210526. [PMID: 35153609 PMCID: PMC8791046 DOI: 10.1098/rspa.2021.0526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/07/2021] [Indexed: 01/16/2023] Open
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.
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Affiliation(s)
- A. Gallet
- Department of Civil and Structural Engineering, University of Sheffield, Sheffield, UK
| | - S. Rigby
- Department of Civil and Structural Engineering, University of Sheffield, Sheffield, UK
| | - T. N. Tallman
- School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, USA
| | - X. Kong
- Department of Physics and Engineering Science, Coastal Carolina University, Conway, SC, USA
| | - I. Hajirasouliha
- Department of Civil and Structural Engineering, University of Sheffield, Sheffield, UK
| | - A. Liew
- Department of Civil and Structural Engineering, University of Sheffield, Sheffield, UK
| | - D. Liu
- School of Physical Sciences, University of Science and Technology of China, Hefei, People’s Republic of China
| | - L. Chen
- Department of Civil and Structural Engineering, University of Sheffield, Sheffield, UK
| | - A. Hauptmann
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
- Department of Computer Science, University College London, London, UK
| | - D. Smyl
- Department of Civil, Coastal, and Environmental Engineering, University of South Alabama, Mobile, AL, USA
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Zaninovic N, Khosravi P, Hajirasouliha I, Malmsten J, Kazemi E, Zhan Q, Toschi M, Elemento O, Rosenwaks Z. Assessing human blastocyst quality using artificial intelligence (AI) convolutional neural network (CNN). Fertil Steril 2018. [DOI: 10.1016/j.fertnstert.2018.07.267] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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