Validation of vibration-based structural health monitoring on retrofitted railway bridge KW51
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Abstract In this paper, the feasibility of structural health monitoring based on natural frequencies is investigated for a steel bowstring railway bridge in Leuven, Belgium. The data used in the study are obtained from an ongoing long-term monitoring campaign on the railway bridge and include acceleration measurements on the bridge deck and the arches. During the monitoring period, the railway bridge has been retrofitted, resulting in data for two distinct states of the structure. Particular attention is paid to removing the effects of environmental conditions, such as temperature, which affect the modal characteristics of the structure and therefore may lead to false-positive or false-negative damage detection. A comparison is made between standard linear regression and robust principal component analysis (PCA), two black-box modeling techniques which are adopted to remove natural frequency variations resulting from changes in the environmental conditions. In order to assess the success rate of these techniques, a receiver operating characteristic (ROC) curve analysis is performed, considering the actual retrofit as well as a number of more subtle structural changes, which are modeled using a detailed finite element model of the structure. The state transition can be observed for the actual retrofit as well as for smaller structural modifications that result in relatively small natural frequency shifts.
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Many existing methods for correcting confounding effects are based on different types of mean regression.
“Many existing methods for correcting confounding effects are based on different types of mean regression.”
From Confounder-adjusted Covariances of System Outputs and Applications to Structural Health Monitoring (2024) · cites this paper as Maes et al., 2022
All existing response surface modeling methods mentioned in the paper focus on mean response values given the confounder(s) and do not normalize higher-order statistics.
“However, all existing methods for response surface modeling mentioned above focus on mean response values given the confounder(s) and do not normalize higher-order statistics.”
From Confounder-adjusted Covariances of System Outputs and Applications to Structural Health Monitoring (2024) · cites this paper as Maes et al., 2022
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Rao, M. E., Simon, J., Moll, J., et al. (2025) · Structural Health Monitoring · doi:10.1177/14759217251327224 · Cited by 5 · see citing works
Mohammed, H. A. (2024) · Journal of Optics · doi:10.1007/s12596-024-01712-5 · Cited by 5 · see citing works
Wu, B., Tang, W., Zhou, Z., et al. (2025) · Structures · doi:10.1016/j.istruc.2025.109777 · Cited by 4 · see citing works
Vision-based geometric finite element model updating for cable suspension bridges
Lee, Y.-W., Lee, J. H., Byun, N., et al. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103714 · Cited by 4 · see citing works
Nguyen, D. C., Salamak, M., Katunin, A., et al. (2023) · Lecture notes in civil engineering · doi:10.1007/978-3-031-39117-0_38 · Cited by 4 · see citing works
Vitale, V. N., Martino, S. D., Peron, A., et al. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-02009-y · Cited by 4 · see citing works
Automated Girder Rotation Assessment of High-Speed Railway Bridges for Operation Safety
Zheng, H., Yi, T., Xu, Z., et al. (2025) · Journal of Bridge Engineering · doi:10.1061/jbenf2.beeng-7121 · Cited by 4 · see citing works
Smart Diagnosis of a Cantilever Beam Using SVM
Katam, R., Kalapatapu, P., & Pasupuleti, V. D. K. (2023) · International Conference on Emerging Techniques in Computational Intelligence (ICETCI) · doi:10.1109/icetci58599.2023.10331129 · Cited by 4 · see citing works
“An examination of how the Receiver Operating Characteristic (ROC) curve is done to determine how effective these strategies are is discussed by Maes, K et al. [17].”
BackgroundZhu, W., Paolacci, F., Quinci, G., et al. (2026) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2026.113876 · Cited by 3 · see citing works
An Optimized Neuro_Fuzzy Based Regression Trees for Disease Prediction Framework
Verma, A., Agarwal, G., Gupta, A. K., et al. (2022) · Applied Sciences · doi:10.3390/app12178487 · Cited by 3 · see citing works
Damage identification in beams using burst video-records during free-vibration
Garrido, H., Codina, R. H., de Borbón, F., et al. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110539 · Cited by 3 · see citing works
del Pozo, G. A., Svendsen, B. T., & Øiseth, O. A. (2023) · Conference proceedings of the Society for Experimental Mechanics · doi:10.1007/978-3-031-36663-5_12 · Cited by 3 · see citing works
Free Vibration Analysis of Arches with Interval-Uncertain Parameters
Nie, Z., Ren, X., Yang, Y., et al. (2023) · Applied Sciences · doi:10.3390/app132212391 · Cited by 3 · see citing works
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SVM-assisted damage identification in cantilever steel beam using vibration-based method
Katam, R., Pasupuleti, V. D. K., & Kalapatapu, P. (2024) · Innovative Infrastructure Solutions · doi:10.1007/s41062-024-01459-9 · Cited by 3 · see citing works
Parametric Study on Structural Damage Classification with Numerically Simulated Vibration Data
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“For evaluating the health state of structures and damage detection or identification, vibration-based [1,2] methodologies are widely used in the field known as Structural Health Monitoring (SHM).”
MethodAmbient vibration dataset of a short-span cable-stayed bridge
Kalhori, H., Alamdari, M. M., Samali, B., Halkon, B., & Rashidi, M. (2024) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2024.2438741 · Cited by 3 · see citing works
Bridge Damage Detection Using Complexity Pursuit and Extreme Value Theory
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Anastasopoulos, D., Maes, K., Roeck, G. D., et al. (2023) · CRC Press eBooks · doi:10.1201/9781003323020-45 · Cited by 2 · see citing works
Self-citationSimulation Error Influence on Damage Identification Classifiers Trained by Numerical Data
Seventekidis, P., Giagopoulos, D., & Koutsoupakis, J. (2023) · Conference proceedings of the Society for Experimental Mechanics · doi:10.1007/978-3-031-34946-1_3 · Cited by 2 · see citing works
Pioneering Remote Sensing in Structural Health Monitoring
Entezami, A., Behkamal, B., & Michele, C. D. (2024) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-031-53995-4_1 · Cited by 2 · see citing works
Direct Analysis of a Steel Railway Bridge via Monitoring System of an Instrumented Structure
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Mahmoudkelayeh, M., Adhami, B., & Razavi, B. S. (2024) · Journal of Performance of Constructed Facilities · doi:10.1061/jpcfev.cfeng-4323 · Cited by 2 · see citing works
- Authors
- Maes, K., Van Meerbeeck, L., Reynders, E. P. B., & Lombaert, G.
- Affiliations
- KU Leuven (BE)
- Corresponding author
- Maes, K.
- Venue
- Mechanical Systems and Signal Processing
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 165
- Pages
- 108380
- ISSN
- 0888-3270 (ISSN-L)
- OpenAlex
- W3197025840
- Semantic Scholar
- 3522f0e21b627d8931c2c2e9f8f534218b94b3e2
- Cited by
- 143
- References
- 30
- Citations by year
- 2026: 23 · 2025: 33 · 2024: 30 · 2023: 40 · 2022: 16 · 2021: 1
- FWCI
- 11.19
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- Locations
- Mechanical Systems and Signal Processing (publishedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- Funders
- Fonds Wetenschappelijk Onderzoek (12Q9218N, 1511719N)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Vibration Analysis
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Engineering and Materials Analysis
- Keywords
- vibration-based structural health monitoring, natural frequencies, railway bridges, bridge retrofitting, robust principal component analysis, linear regression, damage detection, receiver operating characteristic, finite element model, acceleration measurement, long-term monitoring
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