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Structural engineering from an inverse problems perspective
Optimization and Control · Submitted 29 Jun 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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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The abstract, sentence by sentence
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 response given data including external forces, geometry, member sizes, restraint, etc. -- characterising a forward problem (structural causalities $\to$ 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 realisations also hold true in structural health monitoring and a number of structural engineering sub-fields (response $\to$ 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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The field of structural engineering is vast, spanning areas from the design of new infrastructure to the assessment of existing infrastructure. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | From the onset, traditional entry-level university courses teach students to analyse structural response given data including external forces, geometry, member sizes, restraint, etc. -- characterising a forward problem (structural causalities $\to$ structural response). | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s2 | 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. | background 1.00 | 0.05 | 0.03 | asserted 0.99 |
| s3 | Similar inverse realisations also hold true in structural health monitoring and a number of structural engineering sub-fields (response $\to$ structural causalities). | background 0.95 | 0.07 | 0.04 | asserted 0.92 |
| s4 | 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. | contribution 0.91 | 0.24 | 0.28 | hedged possibility 0.85 |
| s5 | To this end, we conclude that the future of inverse problems in structural engineering is inexorably linked to engineering education and machine learning developments. | future work 0.88 | 0.40 | 0.85 | asserted 1.00 |
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The paper
Original 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 response given data including external forces, geometry, member sizes, restraint, etc. -- characterising a forward problem (structural causalities $\to$ 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 realisations also hold true in structural health monitoring and a number of structural engineering sub-fields (response $\to$ 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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How the literature uses it
1 arXiv paper cite this in 1 sentence
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Cites as support 1 paper
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Here, data driven approaches are a powerful alternative to compensate for modelling errors [ref] or reducing computational cost of iterative optimisation schemes by model approximations [ref] .
Model reduction in acoustic inversion by artificial neural network · Machine learned inversion · 0.97
Cites as background 1 paper
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More importantly, especially when taking into consideration the recent developments in data science ranging from CNNs [ref] , transformers [ref] and graph neural networks [ref] , there exists a vast spectrum of knowledge and applications we may not even be aware of.
Attention Is All You Need · Inverse methodology in structural engineering education · 0.87
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