Direct and indirect Structural Health Monitoring of steel railway bridges: A state-of-the-art review and future challenges
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https://orcid.org/0000-0002-7211-4520
Zihao Liu
Mehrisadat Alamdari
Rune Schlanbusch
Abstract
Steel railway bridges are a vital part of the transportation infrastructure, and their normal operation is crucial to a functioning society. However, aging bridges are subjected to traffic loads and harsh environmental conditions, which can lead to deterioration mechanisms. When damages caused by such mechanisms reach a critical level, they can lead to catastrophic bridge failures, high maintenance costs, and loss of human lives. Thus, early damage detection, localization, quantification, and the estimation of the remaining useful life of a bridge are crucial. Structural Health Monitoring (SHM) systems based on vibration measurements have been developed for bridge monitoring. SHM is characterized as direct or indirect (drive-by) depending on how the sensors are used. In direct SHM, vibration sensors are mounted on the bridge to measure the response of the bridge as the trains pass, while in indirect SHM, vibration sensors are installed on passing trains to measure the response of the bridge. The high cost for the deployment and maintenance of direct SHM instrumentation across the large number of bridges in a typical railway network limits its scalability, making network-wide deployment economically impractical. As a result, indirect SHM has been explored as a less costly alternative for network-level monitoring, while direct SHM remains highly valuable for critical assets, high-risk structures, and validating indirect monitoring methods. Despite growing interest, the main research gap is the existence of only two review papers on SHM in steel railway bridges, with the studies referred to in the review papers covering only direct SHM and mainly damage detection, localization, and quantification. The goal of the current review article is to address this research gap by reviewing the state-of-the-art in SHM methods applied to steel railway bridges between 2010 and 2025. The state-of-the-art encompasses direct SHM studies with numerical, experimental, and field validation on full-scale bridges, and indirect SHM studies with numerical and field validation on full-scale bridges and experimental validation on laboratory-scaled bridges.} {Within indirect SHM, frequency identification, which recovers bridge natural frequencies from train-mounted sensors, is treated as an enabling monitoring task that supports SHM workflow. In addition, the review article provides recommendations for future challenges.
How to Cite
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Structural Health Monitoring, Steel, Railway bridges, Damage diagnosis, Damage prognosis, Direct, indirect, Direct Structural Health Monitoring, Indirect Structural Health Monitoring, Damage, Detection, Localization, Quantification, Vibration, Bridge, Railway, Train, Drive-by, review
Anastasopoulos, D., & Reynders, E. P. B. (2023). Modal strain monitoring of the old Nieuwebrugstraat bridge: Local damage versus temperature effects. Engineering Structures, 296, 116854.
Armijo, A., & Zamora-Sanchez, D. (2024). Integration of railway bridge structural health monitoring into the Internet of Things with a digital twin: A case study. Sensors, 24, 2115.
Azim, M., & Gul, M. (2021). Data-driven damage identification technique for steel truss railroad bridges utilizing principal component analysis of strain response. Structure and Infrastructure Engineering: Maintenance, Management, Life-Cycle Design and Performance, 17(8), 1019–1035.
Bernardini, L., Bono, F. M., & Collina, A. (2025a). Drive-by damage detection and localization exploiting continuous wavelet transform and multiple sparse autoencoders. Railway Engineering Science, 33, 721–745.
Bernardini, L., Bono, F. M., & Collina, A. (2025b). Drive-by damage detection based on the use of CWT and sparse autoencoder applied to steel truss railway bridge. Advances in Mechanical Engineering, 17(5), 16878132251339857.
Bernardini, L., Carnevale, M., & Collina, A. (2021). Damage identification in Warren truss bridges by two different time–frequency algorithms. Applied Sciences, 11, 10605.
Bernardini, L., Carnevale, M., Somaschini, C., Matsuoka, K., & Collina, A. (2020). A numerical investigation of new algorithms for the drive-by method in railway bridge monitoring. In Proceedings of the 11th International Conference on Structural Dynamics (EURODYN) (pp. 1033–1043). Athens, Greece.
Bernardini, L., Matsuoka, K., & Collina, A. (2024). Indirect frequency estimation by time-shifted accelerations subtraction: Generalization of the methodology and numerical application on a Warren truss bridge. Journal of Sound and Vibration, 590, 118491.
Beskhyroun, S., Oshima, T., & Mikami, S. (2010). Wavelet-based technique for structural damage detection. Structural Control and Health Monitoring, 17, 473–494.
Beskhyroun, S., Wegner, L. D., & Sparling, B. F. (2012). New methodology for the application of vibration-based damage detection techniques. Structural Control and Health Monitoring, 19, 632–649.
Bragança, C., Souza, D. E. F., Ribeiro, & Bittencourt, T. (2024). Drive-by early damage detection in railway bridges using wavelets and autoencoders. In Proceedings of the 6th International Conference on Railway Technology: Research, Development and Maintenance (Railways) (Vol. 15). Prague, Czech Republic.
Carnevale, M., Collina, A., & Peirlinck, T. (2019). A feasibility study of the drive-by method for damage detection in railway bridges. Applied Sciences, 9, 160.
Chen, Z. W., Zhu, S., Xu, Y. L., Li, Q., & Cai, Q. L. (2014). Damage detection in long suspension bridges using stress influence lines. Journal of Bridge Engineering, 20.
Corbally, R., & Malekjafarian, A. (2023). Detecting changes in the structural behaviour of a laboratory bridge model using the contact-point response of a passing vehicle. Journal of Structural Integrity and Maintenance, 8, 226–238.
Dang, H. V., Tatipamula, M., & Nguyen, H. X. (2022). Cloud-based digital twinning for structural health monitoring using deep learning. IEEE Transactions on Industrial Informatics, 18(6), 3820–3830.
Froseth, G. T., Ronnquist, A., Cantero, D., & Oiseth, O. (2017). Influence line extraction by deconvolution in the frequency domain. Computers and Structures, 189, 21–30.
Frøseth, G., & Rönnquist, A. (2019). Load model of historic traffic for fatigue life estimation of Norwegian railway bridges. Engineering Structures, 200, 109626.
Ghiasi, A., Moghaddam, M. K., Ng, C. T., Sheikh, A. H., & Shi, J. Q. (2022). Damage classification of in-service steel railway bridges using a novel vibration-based convolutional neural network. Engineering Structures, 264, 114474.
Ghiasi, A., Ng, C. T., & Sheikh, A. H. (2022). Damage detection of in-service steel railway bridges using a fine k-nearest neighbor machine learning classifier. Structures, 45, 1920–1935.
Guo, W. C., Orcesi, A. D., Cremona, C. F., Santos, J. P., Yang, S. Z., & L., D. (2012). A vibration-based framework for structural health monitoring of railway bridges. In Proceedings of the 3rd International Symposium on Life-Cycle Civil Engineering (IALCCE). Vienna, Austria.
Hajializadeh, D. (2023). Deep learning-based indirect bridge damage identification system. Structural Health Monitoring, 22(2), 897–912.
Imam, B. M., & Chryssanthopoulos, M. K. (2012). Causes and consequences of metallic bridge failures. Structural Health Monitoring, 22(1), 93–98.
Leander, J., Andersson, A., & Karoumi, R. (2010). Monitoring and enhanced fatigue evaluation of a steel railway bridge. Engineering Structures, 32(3), 854–863.
Lee, J. S., Park, J., Kim, H. M., & Kim, R. E. (2024). Damage detection for railway bridges using time-frequency decomposition and conditional generative model. Computer-Aided Civil and Infrastructure Engineering, 40, 959–977.
Li, Y., Ding, Y., Zhao, H., & Sun, Z. (2022). Data-driven structural condition assessment for high-speed railway bridges using multi-band FIR filtering and clustering. Structures, 41, 1546–1558.
Lorenzen, S. R., Rupp, M. M., & Hubler, C. (2024). Frequency identification using resonance curve-based drive-by monitoring: Field validation. In Proceedings of the 10th European Workshop on Structural Health Monitoring (EWSHM). Potsdam, Germany.
Maes, K., Meerbeeck, L. V., Reynders, E. P. B., & Lombaert, G. (2022). Validation of vibration-based structural health monitoring on retrofitted railway bridge KW51. Mechanical Systems and Signal Processing, 165, 108380.
Malekjafarian, A., McGetrick, P. J., & O’Brien, E. J. (2015). A review of indirect bridge monitoring using passing vehicles. Shock and Vibration, 2015.
Menghini, A., Leander, J., & Castiglioni, C. A. (2023). A local response function approach for the stress investigation of a centenarian steel railway bridge. Engineering Structures, 286, 116116.
Monti, G., Rabi, R. R., Marella, L., & Proietti, S. T. (2025). Data-driven decision support system for the safety management of railway bridge networks. Reliability Engineering & System Safety, 262, 111202.
Neves, A. C., Gonzalez, I., & Karoumi, R. (2022). Development and validation of a data-based SHM method for railway bridges. In A. Cury, D. Ribeiro, F. Ubertini, & M. D. Todd (Eds.), Structural integrity: Structural health monitoring based on data science techniques (Vol. 21, pp. 95–116). New York: Springer.
Nguyen, D. C., Salamak, M., Katunin, A., Poprawa, G., Przystalka, P., & Hypki, M. (2024). Vibration-based SHM of Debica railway steel bridge with optimized ANN and ANFIS. Journal of Constructional Steel Research, 215, 108505.
Oshima, T., Miyamori, Y., Mikami, S., Yamazaki, T., Beskhyroun, S., & Kopacz, M. F. (2013). Small damage detection of real steel bridge by using local excitation method. Journal of Civil Structural Health Monitoring, 3, 307–315.
Pourtarki, A., Ghavifekr, H. B., & Afshin, H. (2023). Study on the dynamic behaviour of Bafgh–Bandar Abbas lane railway bridge for structural health monitoring purpose. Australian Journal of Structural Engineering, 24, 243–253.
Quqa, S., Palermo, A., & Marzani, A. (2024). Damage index based on the strain-to-displacement relation for health monitoring of railway bridges. Computer-Aided Civil and Infrastructure Engineering, 39, 3145–3163.
Rageh, A., Linzell, D. G., & E., A. S. (2018). Automated, strain-based, output-only bridge damage detection. Journal of Civil Structural Health Monitoring, 8, 833–846.
Reiterer, M., Bettinelli, L., Schellander, J., Stollwitzer, A., & Fink, J. (2023). Application of vehicle-based indirect structural health monitoring method to railway bridges: Simulation and in situ test. Applied Sciences, 13, 10928.
Reiterer, M., Bettinelli, L., Stollwitzer, A., Schellander, J., & Fink, J. (2022). Vehicle-based indirect SHM of an Austrian railway bridge: Simulation and in-situ test. In P. Rizzo & A. Milazzo (Eds.), Lecture Notes in Civil Engineering: European Workshop on Structural Health Monitoring (EWSHM), Volume 1 (Vol. 253, pp. 721–731). Switzerland: Springer.
Rupp, M. M., Lorenzen, S. R., Fritzsche, M. A., Riedel, H., Kohl, A., Apostolidi, E., & Schneider, J. (2023). High-speed drive-by monitoring: Field testing with an intercity express train (ICE). Applied Sciences, 6, 854–862.
Sangiorgio, V., Nettis, A., Uva, G., Pellegrino, F., Varum, H., & Adam, J. M. (2022). Analytical fault tree and diagnostic aids for the preservation of historical steel truss bridges. Engineering Failure Analysis, 133, 105996.
Sarmadi, H., Entezami, A., Behkamal, B., & Michele, C. (2022). Partially online damage detection using long-term modal data under severe environmental effects by unsupervised feature selection and local metric learning. Journal of Civil Structural Health Monitoring, 12, 1043–1066.
Siriwardane, S. A. S. C., Ohga, M., Dissanayake, P. B. R., & Kaita, T. (2010). Structural appraisal-based different approach to estimate the remaining fatigue life of railway bridges. Structural Health Monitoring, 9(4), 323–339.
Stour, C. D., Gres, S., Dertimanis, V. K., Ancu, L., & Chatzi, E. N. (2025). Identification of railway bridge modal properties based solely on acceleration data from traversing trains. Mechanical Systems and Signal Processing, 239, 113342.
Svendsen, B. T., Froseth, G. T., Oiseth, O., & Ronnquist, A. (2022). A data-based structural health monitoring approach for damage detection in steel bridges using experimental data. Journal of Civil Structural Health Monitoring, 12, 101–115.
Svendsen, B. T., Oiseth, O., Froseth, G. T., & Ronnquist, A. (2023). A hybrid structural health monitoring approach for damage detection in steel bridges under simulated environmental conditions using numerical and experimental data. Structural Health Monitoring, 22(1), 540–561.
Tochaei, E. N., Fang, Z., Taylor, T., Babanajad, S., & Ansari, F. (2021). Structural monitoring and remaining fatigue life estimation of typical welded crack details in the Manhattan Bridge. Engineering Structures, 231, 111760.
Torres, B., Poveda, P., Ivorra, S., & Estevan, L. (2023). Long-term static and dynamic monitoring to failure scenarios assessment in steel truss railway bridges: A case study. Engineering Failure Analysis, 152, 107435.
Vagnoli, M., Remenyte-Prescott, R., & Andrews, J. (2017). Railway bridge structural health monitoring and fault detection: State-of-the-art methods and future challenges. Structural Health Monitoring, 17, 971–1007.
Wang, Y. W., Ni, Y.-Q., & Wang, S. M. (2022). Structural health monitoring of railway bridges using innovative sensing technologies and machine learning algorithms: A concise review. Intelligent Transportation Infrastructure, 1, liac009.
Yano, M. O., Figueiredo, E., Silva, S. D., Cury, A., & Moldovan, I. (2023). Transfer learning for structural health monitoring in bridges that underwent retrofitting. Buildings, 13(9), 2323.
Ye, X., Su, Y., & Han, J. (2014). A state-of-the-art review on fatigue life assessment of steel bridges. Mathematical Problems in Engineering, 2014, 956473.
Zakharenko, M., Frøseth, G. T., & Ronnquist, A. (2022). How conservative is the Norwegian fatigue load model for service life estimation of railway bridges? Structure and Infrastructure Engineering, 20, 1404–1417.
Zhang, Y., Miyamori, S., Y., Mikami, & Saito, T. (2019). Vibration-based structural state identification by a 1-dimensional convolutional neural network. Computer-Aided Civil and Infrastructure Engineering, 34, 822–839.
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