Vibration-based damage detection of bridges under varying temperature effects using time-series analysis and artificial neural networks
Abstract
Structural health monitoring (SHM) has become a very important research area for evaluating the performances of bridges. An important issue with continuous SHM and damage detection of bridges is the effects of temperature variations on the measurement data, which can produce bigger effects in the response than the damage itself. In this study, a sensor-clustering-based time-series analysis method integrated with artificial neural networks (ANNs) was employed for damage detection under temperature variations. The damage features obtained solely using the time-series-based damage-detection algorithm are very effective for damage assessment; however, they yield false positives and negatives when temperature variations are present. Therefore, ANNs were used to compensate the temperature effects on the damage features obtained from time-series analysis. This methodology is applied to a footbridge finite-element model in which 2,000 simulations with temperature effects and damage cases were conducted. Results reveal that the proposed method can successfully determine the existence, location, and relative severity of damage using output-only vibration and temperature data even when temperature variations are present.
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Kostić et al. [238] integrated a sensor-clustering-based time-series analysis method with ANNs to compensate for the effects of temperature.
“Kostić et al. [238] integrated a sensor-clustering-based time-series analysis method with ANNs to compensate for the effects of temperature.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [238]
Differences between the damage features from the time series and ANN analyses were used for damage detection.
“Differences between the damage features from the time series and ANN analyses were used for damage detection.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [238]
Multilayer ANNs were then trained using the obtained damage features resulting from different temperature scenarios.
“Multilayer ANNs were then trained using the obtained damage features resulting from different temperature scenarios.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [238]
Numerical results demonstrated that the proposed method could successfully determine the existence, location and relative severity of damage under varying temperature conditions.
“Numerical results demonstrated that the proposed method could successfully determine the existence, location and relative severity of damage under varying temperature conditions.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [238]
A sensor-clustering-based ARX method was applied to the free vibration acceleration data to calculate the damage features.
“A sensor-clustering-based ARX method was applied to the free vibration acceleration data to calculate the damage features.”
From Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019 (2021) · cites this paper as [238]
Cites
Modal identification of bridges under varying environmental conditions: Temperature and wind effects
Li, H., Li, S., Ou, J., & Li, H. (2009) · Structural Control and Health Monitoring · doi:10.1002/stc.319 · Cited by 168 · see citing works
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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
Structural health monitoring research under varying temperature condition: a review
Han, Q., Ma, Q., Xu, J., & Liu, M. (2021) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-020-00444-x · Cited by 190 · see citing works
Keshmiry, A., Hassani, S., Mousavi, M., & Dackermann, U. (2023) · Buildings · doi:10.3390/buildings13040918 · Cited by 96 · see citing works
Parziale, M., Lomazzi, L., Giglio, M., & Cadini, F. (2022) · Structural Control and Health Monitoring · doi:10.1002/stc.3076 · Cited by 22 · see citing works
- Authors
- Kostić, B., & Gül, M.
- Affiliations
- University of Alberta (CA)
- Corresponding author
- Gül, Mustafa
- ORCID
- Gül, Mustafa
- Venue
- Journal of Bridge Engineering
- Published
- 2017-10
- Volume
- 22
- Issue
- 10
- ISSN
- 1084-0702 (ISSN-L)
- 1943-5592
- OpenAlex
- W2738020365
- Cited by
- 74
- References
- 1
- Citations by year
- 2026: 2 · 2025: 14 · 2024: 12 · 2023: 6 · 2022: 8 · 2021: 13 · 2020: 15 · 2019: 3 · 2018: 1
- FWCI
- 5.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Bridge Engineering (publishedVersion)
- 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 › Civil and Structural Engineering › Structural Engineering and Vibration Analysis
- Keywords
- vibration-based damage detection, bridge health monitoring, temperature effects, time series analysis, artificial neural networks, sensor clustering, pedestrian bridge, damage localization, damage severity, false positives and false negatives
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