A hybrid structural health monitoring approach for damage detection in steel bridges under simulated environmental conditions using numerical and experimental data
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This paper presents a novel hybrid structural health monitoring (SHM) framework for damage detection in bridges using numerical and experimental data. The framework is based on the hybrid SHM approach and combines the use of a calibrated numerical finite element (FE) model to generate data from different structural state conditions under varying environmental conditions with a machine learning algorithm in a supervised learning approach. An extensive experimental benchmark study is performed to obtain data from a local and global sensor setup on a real bridge under different structural state conditions, where structural damage is imposed based on a comprehensive investigation of common types of steel bridge damage reported in the literature. The experimental data are subsequently tested on the machine learning model. It is demonstrated that relevant structural damage can be established based on the hybrid SHM framework by separately evaluating different cases considering natural frequencies, mode shapes, and mode shape derivatives. Consequently, the work presented in this study represents a significant contribution toward establishing SHM systems that can be applied to existing steel bridges. The proposed framework is applicable to any bridge structure in which relevant structural damage can be simulated and experimental data obtained.
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Modal variations induced by traffic can be considered to arise in dynamic and static forms.
“The modal variations induced by traffic can be considered to arise in dynamic and static forms [200,201].”
From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [201]
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Keshmiry, A., Hassani, S., Mousavi, M., & Dackermann, U. (2023) · Buildings · doi:10.3390/buildings13040918 · Cited by 96 · see citing works
- Authors
- Svendsen, B. T., Øiseth, O., Frøseth, G. T., & Rønnquist, A.
- Affiliations
- Norwegian University of Science and Technology (NO)
- Corresponding author
- Svendsen, Bjørn T.
- Venue
- Structural Health Monitoring
- Publisher
- SAGE Publications
- Published
- 2022-05-20
- Volume
- 22
- Issue
- 1
- Pages
- 540-561
- ISSN
- 1475-9217 (ISSN-L)
- 1741-3168
- OpenAlex
- W4293247957
- Cited by
- 88
- References
- 43
- Citations by year
- 2026: 21 · 2025: 39 · 2024: 16 · 2023: 11 · 2022: 1
- FWCI
- 7.53
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Structural Health Monitoring (publishedVersion, cc-by)
- Funders
- Bane NORThe Norwegian Railway Directorate
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Materials Analysis
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- steel bridges, damage detection, finite element model updating, machine learning, natural frequencies, mode shapes, environmental factors, benchmark tests, existing bridges
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