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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
Robust principal component analysis?
Candès, E. J., Li, X., Ma, Y., & Wright, J. N. (2011) · Journal of the ACM · doi:10.1145/1970392.1970395 · Cited by 7,048
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
(1993) · Cited by 822
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
A velocity-related running safety assessment index in seismic design for railway bridge
Zhao, H., Wei, B., Jiang, L., et al. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110305 · Cited by 69
Entezami, A., Sarmadi, H., Behkamal, B., et al. (2023) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2023.2166538 · Cited by 65
“Accordingly, the result of early damage detection in the Z24 Bridge via the proposed MTUL method is shown in Figure 11, where the horizontal line refers to the threshold limit.”
InfluentialOn statistic alignment for domain adaptation in structural health monitoring
Poole, J., Gardner, P. A., Dervilis, N., Bull, L. A., & Keith Worden, B. (2022) · Structural Health Monitoring · doi:10.1177/14759217221110441 · Cited by 56
Domain-adapted Gaussian mixture models for population-based structural health monitoring
Gardner, P. A., Bull, L. A., Dervilis, N., & Worden, K. (2022) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-022-00565-5 · Cited by 53
“Novelty detection has been successfully performed on this dataset using robust principal component analysis (PCA) and linear regression methods [33].”
MethodOptimal Sensor Placement for Structural Health Monitoring: A Comprehensive Review
Sun, Z., Mahmoodian, M., Sidiq, A., Jayasinghe, S., Shahrivar, F., & Setunge, S. (2025) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan14020022 · Cited by 53
“Currently, comprehensive research has been carried out on the front and back ends of the SHM and maintenance system, including various types of sensors or algorithms, e.g., vibration-based sensors [21], fibre optic sensors [22], or different sensor network optimisation strategies [23].”
Ahmadian, V., Aval, S. B. B., Noori, M. N., Wang, T., & Altabey, W. A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107226 · Cited by 46
Behkamal, B., Entezami, A., Michele, C. D., & Arslan, A. N. (2023) · Remote Sensing · doi:10.3390/rs15143503 · Cited by 45
Probabilistic autoencoder-based bridge damage assessment using train-induced responses
Sarwar, M. Z., & Cantero, D. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.111046 · Cited by 50
Anomaly detection in long-term tunnel deformation monitoring
Maes, K., Salens, W., Feremans, G., et al. (2021) · Engineering Structures · doi:10.1016/j.engstruct.2021.113383 · Cited by 44
Self-citationA general data quality evaluation framework for dynamic response monitoring of long-span bridges
Deng, Y., Ju, H., Zhong, G., Li, A., & Ding, Y. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110514 · Cited by 44
Entezami, A., Sarmadi, H., & Behkamal, B. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.118722 · Cited by 43
Sarmadi, H., Entezami, A., Behkamal, B., & Michele, C. D. (2022) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-022-00596-y · Cited by 42
“Because the bridge was equipped with a SHM system from October 2, 2018, it is possible to utilize the vibration measurements before and after retrofitting as undamaged and damaged conditions [47].”
BackgroundMethodInfluentialSeventekidis, P., & Giagopoulos, D. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2022.109741 · Cited by 37
Nguyen, D. C., Salamak, M., Katunin, A., Poprawa, G., & Gerges, M. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.118431 · Cited by 39
Anastasopoulos, D., Reynders, E. P. B., François, S., Roeck, G. D., Lysebetten, G. V., Itterbeeck, P. V., & Huybrechts, N. (2022) · Composite Structures · doi:10.1016/j.compstruct.2022.115295 · Cited by 35
Self-citationUnsupervised long-term damage detection in an uncontrolled environment through optimal autoencoder
Yang, K., Kim, S., & Harley, J. B. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110473 · Cited by 35
Tomassini, E., García‐Macías, E., Reynders, E. P. B., et al. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110587 · Cited by 33
Self-citationHuang, J., Yuan, S.-J., Li, D., & Li, H. (2022) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2022.117516 · Cited by 31
Influence of frost and local stiffness variations on the strain mode shapes of a steel arch bridge
Anastasopoulos, D., Maes, K., Roeck, G. D., et al. (2022) · Engineering Structures · doi:10.1016/j.engstruct.2022.115097 · Cited by 29
Self-citationMaes, K., & Lombaert, G. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110142 · Cited by 24
Self-citationDamage detection under environmental and operational variability using the cointegration technique
Pirrò, M., Pereira, S., Gentile, C., Magalhães, F., & Cunha, Á. A. M. F. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.119700 · Cited by 22
Sresakoolchai, J., Hamarat, M. Z., & Kaewunruen, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-29292-7 · Cited by 20
Extracting Bridge Frequencies from The Dynamic Responses of Moving and Non-moving Vehicles
Zhang, T., Xiong, Z., Zhu, J. F., Zheng, K., Wu, M., & Li, Y. (2023) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2023.117865 · Cited by 23
Torres, B., Poveda-Martínez, P., Ivorra, S., & Estevan, L. (2023) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2023.107435 · Cited by 19
Quqa, S., Lasri, O., Delo, G., Giordano, P. F., Surace, C., Marzani, A., & Limongelli, M. P. (2025) · Structural Health Monitoring · doi:10.1177/14759217241310525 · Cited by 24
“…structures and a diverse real-world population, which includes data from the Z24 post-tensioned concrete two-cell box-girder road bridge 167 and the KW51 steel single-span tied arch railway bridge, 168 for which datasets collected in separate experimental campaigns are available under request.”
Dalmasso, M., Civera, M., Biagi, V. D., et al. (2025) · Advances in Bridge Engineering · doi:10.1186/s43251-025-00169-1 · Cited by 17
“…several studies above this bridge; for instance, one study involving the feasibility of an SHM system, which relies on the natural frequencies (Maes et al. 2022), the analysis of the pot bearing with its nonlinear behaviour (van de Velde et al. 2023), a continuous dynamic strain monitoring of…”
Huang, J. S., Li, D., & Li, H. (2023) · Measurement · doi:10.1016/j.measurement.2023.112682 · Cited by 18
Pei, X.-Y., Hou, Y., Huang, H.-B., & Zheng, J.-X. (2025) · Buildings · doi:10.3390/buildings15050763 · Cited by 17
“Further advancements in PCA-based techniques, such as nonlinear PCA [31], robust PCA [32], and subspace PCA [33], have enhanced damage detection accuracy.”
Harle, S. M., Bhagat, A. P., Ingole, R., & Zanjad, N. S. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10276-x · Cited by 19
Khodadoost, S., Nouhi, B., Talatahari, S., & Sareh, P. (2025) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2025.116886 · Cited by 17
Ghiasi, R., & Malekjafarian, A. (2025) · Railway Engineering Science · doi:10.1007/s40534-025-00380-w · Cited by 15
Chen, Z.-H., Xu, Z., Entezami, A., Sarmadi, H., Gao, W., & Behkamal, B. (2026) · Engineering Structures · doi:10.1016/j.engstruct.2026.122971 · Cited by 22
van de Velde, M., Anastasopoulos, D., Backer, H. D., Reynders, E. P. B., & Lombaert, G. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.119832 · Cited by 13
Self-citationMitigation of model error effects in neural network-based structural damage detection
Ponsi, F., Bassoli, E., & Vincenzi, L. (2023) · Frontiers in Built Environment · doi:10.3389/fbuil.2022.1109995 · Cited by 12
Vibration-based SHM of Dębica railway steel bridge with optimized ANN and ANFIS
Nguyen, D. C., Salamak, M., Katunin, A., et al. (2024) · Journal of Constructional Steel Research · doi:10.1016/j.jcsr.2024.108505 · Cited by 12
Lu, R., & Judd, J. P. (2022) · Infrastructures · doi:10.3390/infrastructures7020016 · Cited by 12
“For bridges, structural health monitoring systems have also been used to determine natural frequencies [8], assess the condition of the substructure [9], and detect other damage [10].”
BackgroundSaha, S., Katyal, K., & Somala, S. N. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113088 · Cited by 13
Mariniello, G., Pastore, T., & Asprone, D. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106031 · Cited by 11
Performance Warning of Bridges under Train Actions through Equivalent Frequency Response Functions
Yang, X., Yi, T., Qu, C., et al. (2022) · Journal of Bridge Engineering · doi:10.1061/(asce)be.1943-5592.0001925 · Cited by 11
Wang, D., & Yang, S. X. (2022) · Intelligence & Robotics · doi:10.20517/ir.2022.25 · Cited by 11
“In engineering practice, the health monitoring of bridge cracks has attracted the attention of relevant national departments and research institutions([5,6]), corresponding norms and standards have been issued, and the monitoring methods and monitoring parameters for bridge deformation and cracks have been clearly spe…”
BackgroundBerardengo, M., Lucà, F., Vanali, M., & Annesi, G. (2023) · Sensors · doi:10.3390/s23031154 · Cited by 11
Detection and localization of anomalies in bridges using accelerometer data and sparse auto-encoders
Pirrò, M., & Gentile, C. (2025) · Developments in the Built Environment · doi:10.1016/j.dibe.2025.100715 · Cited by 10
Cosoli, G., Martarelli, M., Mobili, A., et al. (2023) · Sensors · doi:10.3390/s23229292 · Cited by 10
Laha, S. K., Swarnakar, B., & Kansabanik, S. (2024) · Asian Journal of Civil Engineering · doi:10.1007/s42107-024-01006-y · Cited by 11
“Maes et al. (2022) in their study examined the feasibility of using natural frequencies for SHM on a steel bowstring railway bridge in Leuven, Belgium.”
Bridge monitoring using mobile sensing data with traditional system identification techniques
Cronin, L., Sen, D., Marasco, G., Matarazzo, T. J., & Pakzad, S. N. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13358 · Cited by 12
“…Adeli, Valtierra-Rodriguez, Camarena-Martinez, et al., 2016), and subsequently analyzing damage-sensitive features (modal properties or a parameterized function) (Maes et al., 2022; Magalhães et al., 2012; Montalvao et al., 2006) and finite element model updating (Teughels & De Roeck, 2004).”
Huang, J. S., Liu, Y., Qi, H., Chen, S., & Li, D. (2024) · Structures · doi:10.1016/j.istruc.2024.108118 · Cited by 10
Pei, X.-Y., Huang, H., & Cao, P. (2025) · Buildings · doi:10.3390/buildings15030359 · Cited by 10
Turrisi, S., Zappa, E., & Cigada, A. (2022) · Sensors · doi:10.3390/s22062177 · Cited by 10
“In [39], indeed, natural frequencies are used to evaluate the performance of linear regression and robust principal component analysis as damage detection techniques.”
MethodAnomaly detection in railway bridges using imaging techniques
Russo, P., & Schaerf, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-30683-z · Cited by 10
“Moreover, the analysis performed in 5 is quite specific to the KW51 bridge, thus not leading to a general methodology that can be applied in a number of different situations.”
BackgroundMethodComputationally Efficient Structural Health Monitoring Using Graph Signal Processing
Cheema, M. A., Sarwar, M. Z., Gogineni, V. C., Cantero, D., & Rossi, P. S. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3366346 · Cited by 10
“We considered signals from a bridge in Leuven, Belgium, known as the KW51 railway bridge [42].”
MethodPhusakulkajorn, W., Zeng, Y., Li, Z., & Núñez, A. A. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3557712 · Cited by 13
“Several unsupervised learning approaches have been developed to analyze vibration data, such as t-distributed stochastic neighbor embedding (t-SNE) [29], K-means clustering [30], principal component analysis (PCA) [31], tensor clustering [32], and one-class support vector machine (OC-SVM) [33].”
Bel-Hadj, Y., Weijtjens, W., & Devriendt, C. (2025) · Structural Health Monitoring · doi:10.1177/14759217251324194 · Cited by 11
3-D Microwave Displacement Sensor: 3-D Displacement Measurement Using a Single Microwave Transceiver
Gou, Y., Xiong, Y., Ren, Z., Meng, G., & Peng, Z. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3481558 · Cited by 9
“The accurate measurement of displacement is highly desired in various fields, such as civil structure health monitoring [3], smart manufacturing [4], [5], and aerospace [6].”
BackgroundZunino, L., Casas, J. R., & Domaneschi, M. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.120965 · Cited by 8
Damage Identification in Long-Span Cable-Stayed Bridges Under Multiple Support Excitations
Bakhshizadeh, A., Sadeghi, K., Ahmadi, S., et al. (2023) · International Journal of Civil Engineering · doi:10.1007/s40999-023-00823-7 · Cited by 8
“The results showed that strengthening leads to relatively small natural frequency shifts [18].”
BackgroundIntegrated Bridge Structural Health Monitoring System
Elmi, M., Ghafory‐Ashtiany, M., & Bahar, O. (2023) · Iranian Journal of Science and Technology Transactions of Civil Engineering · doi:10.1007/s40996-023-01304-6 · Cited by 8
“Based on the capabilities of the selected method, one or more of the following questions can be addressed (Rytter 1993; Maes et al. 2022): • Detection—”
InfluentialA Review on Technological Advancements in the Field of Data Driven Structural Health Monitoring
Katam, R., Kalapatapu, P., & Pasupuleti, V. D. K. (2022) · Lecture notes in civil engineering · doi:10.1007/978-3-031-07322-9_38 · Cited by 9
Le, N. T., Keenan, M., Nguyen, A., et al. (2024) · Structures · doi:10.1016/j.istruc.2024.108005 · Cited by 7
Anastasopoulos, D., & Reynders, E. P. B. (2024) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-024-00888-5 · Cited by 9
Self-citationModelling the Nonlinear Behavior of the Pot Bearings of Railway Bridge KW51
van de Velde, M., Maes, K., & Lombaert, G. (2023) · Journal of Bridge Engineering · doi:10.1061/jbenf2.beeng-6144 · Cited by 7
Self-citationHerrera, A. R., Alvarez-Montoya, J., Restrepo, J., Herrera, C., Rodríguez, S., Escobar, C. A., Vásquez, R. E., & Sierra-Pérez, J. (2024) · Aerospace · doi:10.3390/aerospace11090708 · Cited by 6
“By utilizing a network of sensors and advanced data analytics, SHM can provide real-time insights into equipment performance, alerting operators to potential failures or inefficiencies before they occur [48,49].”
Huang, J., Yang, J. W., Li, D., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-08206-9 · Cited by 6
Nikdel, A., & Shariatmadar, H. (2024) · Structural Health Monitoring · doi:10.1177/14759217231225402 · Cited by 6
“32,33 Therefore, it seems that the output-only data normalization is more beneficial for removing the environmental and/or operational variability.”
Tracking time-varying properties using quasi time-invariant models with Bayesian dynamic programming
Yang, Y., Zhu, Z., & Au, S. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111546 · Cited by 6
Vélez, A. P., Muñiz, M., & Fernández‐Martínez, J. L. (2024) · AIMS Mathematics · doi:10.3934/math.20241472 · Cited by 6
A demand-capacity approach to define failure thresholds in anomaly detection monitoring systems
Kamali, S., & Marzani, A. (2024) · Journal of dynamic disasters. · doi:10.1016/j.jdd.2024.100004 · Cited by 6
Introduction to Monitoring of Bridge Infrastructure Using Soft Computing Techniques
Gordan, M., Sabbagh-Yazdi, S.-R., Ghaedi, K., et al. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.104905 · Cited by 6
Covariate-adjusted functional data analysis for structural health monitoring
Wittenberg, P., Neumann, L., Mendler, A., & Gertheiss, J. (2025) · Data-Centric Engineering · doi:10.1017/dce.2025.18 · Cited by 5
AI-based structural health monitoring systems
Keshmiry, A., Hassani, S., & Dackermann, U. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13191-2.00008-0 · Cited by 7
Real-time onboard propeller fault diagnosis of autonomous delivery drones through vibration analysis
Rao, M. E., Simon, J., Moll, J., et al. (2025) · Structural Health Monitoring · doi:10.1177/14759217251327224 · Cited by 5
Mohammed, H. A. (2024) · Journal of Optics · doi:10.1007/s12596-024-01712-5 · Cited by 5
Wu, B., Tang, W., Zhou, Z., et al. (2025) · Structures · doi:10.1016/j.istruc.2025.109777 · Cited by 4
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
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
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
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
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
“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
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
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
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
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
Wu, B., OuYang, Y., Chen, H.-P., et al. (2024) · Structures · doi:10.1016/j.istruc.2024.106142 · Cited by 3
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
Parametric Study on Structural Damage Classification with Numerically Simulated Vibration Data
Seventekidis, P., Karyofyllas, G., & Giagopoulos, D. (2024) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2647/2/022004 · Cited by 3
“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
Bridge Damage Detection Using Complexity Pursuit and Extreme Value Theory
Liu, X., Zhuo, W., & Yang, J. (2023) · Buildings · doi:10.3390/buildings13092183 · Cited by 2
Huang, J., Yuan, S., Li, D., & Jiang, T. (2025) · Structural Control and Health Monitoring · doi:10.1155/stc/4684985 · Cited by 2
Neumann, L., Wittenberg, P., Mendler, A., & Gertheiss, J. (2026) · Engineering Structures · doi:10.1016/j.engstruct.2026.122865 · Cited by 2
Anastasopoulos, D., Maes, K., Roeck, G. D., et al. (2023) · CRC Press eBooks · doi:10.1201/9781003323020-45 · Cited by 2
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
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
Direct Analysis of a Steel Railway Bridge via Monitoring System of an Instrumented Structure
Aspar, W. A. N., Pradono, M. H., Barasa, W., et al. (2024) · International Journal on Advanced Science Engineering and Information Technology · doi:10.18517/ijaseit.14.1.19148 · Cited by 2
Mahmoudkelayeh, M., Adhami, B., & Razavi, B. S. (2024) · Journal of Performance of Constructed Facilities · doi:10.1061/jpcfev.cfeng-4323 · Cited by 2
Hu, S., Yang, J. K., Huang, J., Li, D., & Li, C. (2025) · Sensors · doi:10.3390/s25051332 · Cited by 2
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
