A review of nonlinear dynamics applications to structural health monitoring
Abstract
The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). In many cases damage causes a structure that initially behaves in a predominantly linear manner to exhibit nonlinear response when subject to its operating environment. The formation of cracks that subsequently open and close under operating loads is an example of such damage. The damage detection process can be significantly enhanced if one takes advantage of these nonlinear effects when extracting damage-sensitive features from measured data. This paper will provide a review of examples from nonlinear dynamical systems theory and from nonlinear system identification techniques that are used for the feature-extraction portion of the damage detection process. This paper is not intended as a comprehensive review of all damage detection methods rooted in nonlinear dynamics, but rather to provide a number of illustrations of complimentary approaches where damage-sensitive data features are based on nonlinear system response. These features, in turn, can either be used as a direct diagnosis of damage or as input to statistical damage classifier. Copyright © 2007 John Wiley & Sons, Ltd.
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In practice, structures may behave nonlinearly in the undamaged state because of complex joints and interfaces.
“However, in practice, structures may behave nonlinearly in the undamaged state because of complex joints and interfaces [246].”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [246]
Another common type of nonlinearity in structural systems is the yielding of steel members during an earthquake.
“Another common type of nonlinearity in structural systems is the yielding of steel members during an earthquake [246].”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [246]
Cites
Los Alamos National Lab., N. (. S., Doebling, S., USDOE, W., Farrar, C., Prime, M., & Shevitz, D. (1996) · doi:10.2172/249299 · Cited by 2,899 · cited 4× · see citing works
“Identifying features that can accurately distinguish a damaged structure from an undamaged one is the focus of most SHM technical literature [2,3].”
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Hou, R., & Xia, Y. (2021) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2020.115741 · Cited by 674 · see citing works
Shiki, S. B., da Silva, S., & Todd, M. D. (2017) · Structural Health Monitoring · doi:10.1177/1475921716662142 · Cited by 35 · see citing works
Self-citation
- Authors
- Worden, K., Farrar, C. R., Haywood, J., & Todd, M.
- Affiliations
- Los Alamos National Laboratory (US)
- University of Sheffield (GB)
- University of California San Diego (US)
- Venue
- Structural Control and Health Monitoring
- Publisher
- Hindawi Limited
- Published
- 2008
- Volume
- 15
- Issue
- 4
- Pages
- 540-567
- ISSN
- 1545-2255 (ISSN-L)
- 1545-2263
- OpenAlex
- W2098312962
- Cited by
- 212
- References
- 1
- Citations by year
- 2026: 9 · 2025: 9 · 2024: 11 · 2023: 20 · 2022: 18 · 2021: 20 · 2020: 14 · 2019: 12 · 2018: 14 · 2017: 10 · 2016: 13 · 2015: 14 · 2014: 17 · 2013: 4 · 2012: 8
- FWCI
- 11.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Structural Control and Health Monitoring (publishedVersion)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Mechanics of Materials › Ultrasonics and Acoustic Wave Propagation
- Keywords
- structural health monitoring, damage detection, nonlinear dynamics, nonlinear system identification, damage-sensitive features, crack formation, nonlinear structural response, damage classification
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