Ensemble classification method for structural damage assessment under varying temperature
Abstract
Vibration-based damage assessment approaches use modal parameters, such as frequency response functions, mode shapes, and natural frequencies, as indicators of structural damage. Nevertheless, these parameters are sensitive not only to damage but also to temperature variations. Most civil engineering structures are exposed to varying environmental conditions, thus hindering vibration-based damage assessment. Therefore, in this article, a new damage assessment algorithm based on pattern recognition is proposed to scrutinize the healthy state of a structure in the presence of uncertainties such as noise and temperature. The algorithm adopts a combination of couple sparse coding and deep neural network as an ensemble system to assess damage. The proposed method is validated using a numerical model of a truss bridge and experimental data of the I-40 bridge. The results demonstrate its efficiency in the localization and quantification of damages under varying temperature conditions.
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The simulated FRF data was first generated from a numerical model, and PCA was applied to decrease the dimension of FRF data and extract the features.
“The simulated FRF data was first generated from a numerical model, and PCA was applied to decrease the dimension of FRF data and extract the features.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [239]
The couple sparse coding and deep NN were then individually trained.
“The couple sparse coding and deep NN were then individually trained.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [239]
Fallahian et al. [239] combined couple sparse coding and deep NNs to assess damage by considering uncertainties, such as noise and temperature.
“Fallahian et al. [239] combined couple sparse coding and deep NNs to assess damage by considering uncertainties, such as noise and temperature.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [239]
The outputs were combined by with the weight majority voting method to make a better decision about the healthy state of the structure.
“The outputs were combined by with the weight majority voting method to make a better decision about the healthy state of the structure.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [239]
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Review on vibration-based damage detection of vehicular bridges considering traffic effects
Aghelizadeh, P., Tang, Y., Sychterz, A. C., & Sneed, L. H. (2026) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-026-01102-4
- Authors
- Fallahian, M., Khoshnoudian, F., & Meruane, V.
- Affiliations
- Amirkabir University of Technology (IR)
- University of Chile (CL)
- Corresponding author
- Khoshnoudian, Faramarz
- Venue
- Structural Health Monitoring
- Publisher
- SAGE Publications
- Published
- 2017-07-07
- Volume
- 17
- Issue
- 4
- Pages
- 747-762
- ISSN
- 1475-9217 (ISSN-L)
- 1741-3168
- OpenAlex
- W2748435964
- Cited by
- 60
- References
- 53
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 8 · 2023: 9 · 2022: 7 · 2021: 10 · 2020: 12 · 2019: 3 · 2018: 1
- FWCI
- 4.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Structural Health Monitoring (publishedVersion)
- Universidad de Chile (submittedVersion)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Keywords
- structural damage assessment, vibration-based damage detection, temperature variation, ensemble classification, sparse coding, deep neural network, pattern recognition, modal parameters, damage localization, damage quantification, truss bridges, noise robustness
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