The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
The application of machine learning to structural health monitoring
Abstract
In broad terms, there are two approaches to damage identification. Model-driven methods establish a high-fidelity physical model of the structure, usually by finite element analysis, and then establish a comparison metric between the model and the measured data from the real structure. If the model is for a system or structure in normal (i.e. undamaged) condition, any departures indicate that the structure has deviated from normal condition and damage is inferred. Data-driven approaches also establish a model, but this is usually a statistical representation of the system, e.g. a probability density function of the normal condition. Departures from normality are then signalled by measured data appearing in regions of very low density. The algorithms that have been developed over the years for data-driven approaches are mainly drawn from the discipline of pattern recognition, or more broadly, machine learning. The object of this paper is to illustrate the utility of the data-driven approach to damage identification by means of a number of case studies.
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Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
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 3×
“A good summary of the initial efforts in the field is given in Doebling et al. (1996) that exhaustively surveys the field of damage identification (using vibration data) up to1995, citing 20papers that applyneural networks alone.”
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Cited by
Effects of environmental and operational variability on structural health monitoring
Sohn, H. (2007) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2006.1935 · Cited by 883
Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2022) · Structural Health Monitoring · doi:10.1177/14759217211036880 · Cited by 607
Martinez-Luengo, M., Kolios, A., & Wang, L. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.05.085 · Cited by 310
Broer, A. A. R., Benedictus, R., & Zarouchas, D. (2022) · Aerospace · doi:10.3390/aerospace9040183 · Cited by 86
Cofre-Martel, S., Kobrich, P., Lopez Droguett, E., & Meruane, V. (2019) · Shock and Vibration · doi:10.1155/2019/9859281 · Cited by 65
Metadata
- Authors
- Worden, K., & Manson, G.
- Venue
- Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Publisher
- The Royal Society
- Published
- 2006-12-12
- Volume
- 365
- Issue
- 1851
- Pages
- 515-537
- ISSN
- 1364-503X (ISSN-L)
- 1471-2962
- OpenAlex
- W2039488112
- Cited by
- 433
- References
- 2
- FWCI
- 15.94
- Type
- journal article
- Language
- en
- Open access
- closed
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- Worden, K., & Manson, G. (2007). The application of machine learning to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. https://doi.org/10.1098/rsta.2006.1938
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