The DOI matches its Crossref record; 26 of the 30 references it cites were found in a registry or an index.
Validation of vibration-based structural health monitoring on retrofitted railway bridge KW51
Open accessAbstract
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.”
From Confounder-adjusted Covariances of System Outputs and Applications to Structural Health Monitoring (2024) · cites this paper as Maes et al., 2022
In the citing paper
“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
Citations
Cites
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Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
Robust principal component analysis?
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On the generalized distance in statistics
Mahalanobis, P. C. (1936) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 6,068
REFERENCE-BASED STOCHASTIC SUBSPACE IDENTIFICATION FOR OUTPUT-ONLY MODAL ANALYSIS
Peeters, B., & Roeck, G. D. (1999) · Mechanical Systems and Signal Processing · doi:10.1006/mssp.1999.1249 · Cited by 1,533
THE MODAL ASSURANCE CRITERION–TWENTY YEARS OF USE AND ABUSE
Allemang, R. J. (2003) · Sound&Vibration · Cited by 1,159
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Magalhães, F., Cunha, A., & Caetano, E. (2012) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2011.06.011 · Cited by 655
Deraemaeker, A., Reynders, E. P. B., Roeck, G. D., & Kullaa, J. (2007) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2007.07.004 · Cited by 584
Self-citationStructural damage diagnosis under varying environmental conditions—Part I: A linear analysis
Yan, A.-M., Kerschen, G., Boe, P. D., & Golinval, J. (2005) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2004.12.002 · Cited by 537
Online automatic identification of the modal parameters of a long span arch bridge
Magalhães, F., Cunha, Á., & Caetano, E. (2009) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2008.05.003 · Cited by 506
Cited by
Sorted by citations
State-of-the-art review on advancements of data mining in structural health monitoring
Gordan, M., Sabbagh-Yazdi, S.-R., Ismail, Z., Ghaedi, K., Carroll, P., McCrum, D. P., & Samali, B. (2022) · Measurement · doi:10.1016/j.measurement.2022.110939 · Cited by 155
A systematic review of data fusion techniques for optimized structural health monitoring
Hassani, S., Dackermann, U., Mousavi, M., & Li, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102136 · Cited by 169
Entezami, A., Sarmadi, H., & Behkamal, B. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2023.115616 · Cited by 102
A review on structural health monitoring: past to present
Katam, R., Pasupuleti, V. D. K., & Kalapatapu, P. (2023) · Innovative Infrastructure Solutions · doi:10.1007/s41062-023-01217-3 · Cited by 105
Recent vibration-based structural health monitoring on steel bridges: Systematic literature review
Azhar, A. S., Kudus, S. A., Jamadin, A., Mustaffa, N. K., & Sugiura, K. (2023) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2023.102501 · Cited by 79
Metadata
- 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
- 155
- References
- 30
- Citations by year
- 2026: 31 · 2025: 35 · 2024: 30 · 2023: 41 · 2022: 17
- FWCI
- 10.95
- 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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