Abstract
This chapter addresses unsupervised damage detection in railway bridges by presenting a novel AI-based SHM strategy using traffic-induced dynamic responses. To achieve this goal a hybrid combination of wavelets, PCA, and cluster analysis is implemented. Damage-sensitive features from train-induced dynamic responses are extracted and allow taking advantage not only of the repeatability of the loading, but also, of its large magnitude, thus enhancing sensitivity to small-magnitude structural changes. The effectiveness of the proposed methodology is validated in a long-span bowstring-arch railway bridge with a permanent structural monitoring system installed. A digital twin of the bridge was used, along with experimental values of temperature, noise, trains loadings, and speeds, to realistically simulate baseline and damage scenarios. The methodology proved highly sensitive in detecting early damage, even in case of small stiffness reductions that do not impair structural safety, as well as highly robust to false detections. The ability to identify early damage, imperceptible in the original signals, while avoiding observable changes induced by environmental and operational variations, is achieved by carefully defining the modelling and fusion sequence of the information. A damage detection strategy capable of characterizing multi-sensor data while being sensitive to identify local changes is proposed as a tool for real-time structural assessment of bridges without interfering with the normal service condition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Melo LRT, Ribeiro D, Calçada R, Bittencourt TN (2020) Validation of a vertical train–track–bridge dynamic interaction model based on limited experimental data. Struct Infrastruct Eng 16(1):181–201. https://doi.org/10.1080/15732479.2019.1605394
Meixedo A, Ribeiro D, Calçada R, Delgado R (2014) Global and local dynamic effects on a railway viaduct with precast deck. In: Proceedings of the second international conference on railway technology: research, development and maintenance. Civil-Comp Press, Stirlingshire. https://doi.org/10.4203/ccp.104.77
Rytter A (1993) Vibrational based inspection of civil engineering structures. Dept. of Building Technology and Structural Engineering, Aalborg University, Aalborg
Meixedo A, Alves V, Ribeiro D, Cury A, Calçada R (2016) Damage identification of a railway bridge based on genetic algorithms. In: Maintenance, monitoring, safety, risk and resilience of bridges and bridge networks—proceedings of the 8th international conference on bridge maintenance, safety and management, IABMAS 2016, Foz Do Iguaçu; Brazil
Cury A, Cremona C (2012) Assignment of structural behaviours in long-term monitoring: application to a strengthened railway bridge. Struct Health Monit 11(4):422–441. https://doi.org/10.1177/1475921711434858
Posenato D, Kripakaran P, Smith IFC (2010) Methodologies for model-free data interpretation of civil engineering structures. Comput Struct 88(7–8):467–482. https://doi.org/10.1016/j.compstruc.2010.01.001
Meixedo A, Santos J, Ribeiro D, Calçada R, Todd M (2021) Damage detection in railway bridges using traffic-induced dynamic responses. Eng Struct 238(112189). https://doi.org/10.1016/j.engstruct.2021.112189
Mujica LE, Gharibnezhad F, Rodellar J, Todd M (2020) Considering temperature effect on robust principal component analysis orthogonal distance as a damage detector. Struct Health Monit 19(3):781–795. https://doi.org/10.1177/1475921719861908
Cavadas F, Smith IFC, Figueiras J (2013) Damage detection using data-driven methods applied to moving-load responses. Mech Syst Signal Process 39(1–2):409–425. https://doi.org/10.1016/j.ymssp.2013.02.019
Santos JP, Crémona C, Orcesi AD, Silveira P (2013) Multivariate statistical analysis for early damage detection. Eng Struct 56:273–285. https://doi.org/10.1016/j.engstruct.2013.05.022
Hu WH, Moutinho C, Caetano E, Magalhães F, Cunha Á (2012) Continuous dynamic monitoring of a lively footbridge for serviceability assessment and damage detection. Mech Syst Signal Process 33(November):38–55. https://doi.org/10.1016/j.ymssp.2012.05.012
Farrar CR, Worden K (2013) Structural health monitoring: a machine learning perspective. Wiley, New York, pp 1–45
De LOR, Omenzetter P (2010) Damage classification and estimation in experimental structures using time series analysis and pattern recognition. Mech Syst Signal Process 24(5):1556–1569. https://doi.org/10.1016/j.ymssp.2009.12.008
Gonzalez I, Karoumi R (2015) BWIM aided damage detection in bridges using machine learning. J Civ Struct Heal Monit 5(5):715–725. https://doi.org/10.1007/s13349-015-0137-4
Cardoso R, Cury A, Barbosa F (2019) Automated real-time damage detection strategy using raw dynamic measurements. Eng Struct 196(109364). https://doi.org/10.1016/j.engstruct.2019.109364
Azim R, Gül M (2019) Damage detection of steel girder railway bridges utilizing operational vibration response. Struct Control Health Monit 26(e2447):1–15. https://doi.org/10.1002/stc.2447
Nie Z, Lin J, Li J, Hao H, Ma H (2019) Bridge condition monitoring under moving loads using two sensor measurements. Struct Health Monit 19(3):917–937. https://doi.org/10.1177/1475921719868930
Farrar CR, Doebling SW, Nix DA (2001) Vibration–based structural damage identification. Philos Trans R Soc London A: Math Phys Eng Sci 359(1778):131–149. https://doi.org/10.1098/rsta.2000.0717
ANSYS. Academic Research. Release 17.1 2016
Meixedo A, Ribeiro D, Santos J, Calçada R, Todd M (2021) Progressive numerical model validation of a bowstring-arch railway bridge based on a structural health monitoring system. J Civ Struct Heal Monit 11(2):421–449. https://doi.org/10.1007/s13349-020-00461-w
Min X, Santos L (2011) Ensaios dinâmicos da ponte ferroviária sobre o rio sado na variante de alcácer. Lisboa [Portuguese]
Meixedo A, Gonçalves A, Calçada R, Gabriel J, Fonseca H, Martins R (2016) On-line monitoring system for tracks. In: exp.at 2015—3rd experiment international conference, Sao Miguel Island, Azores. https://doi.org/10.1109/EXPAT.2015.7463240
Pimentel R, Ribeiro D, Matos L, Mosleh A, Calçada R (2020) Bridge weigh-in-motion system for the identification of train loads using fiber-optic technology. Structures 2021(30):1056–1070. https://doi.org/10.1016/j.istruc.2021.01.070
Ren WX, Sun ZS (2008) Structural damage identification by using wavelet entropy. Eng Struct 30:2840–2849. https://doi.org/10.1016/j.engstruct.2008.03.013
Cohen A, Ryan RD (1995) Wavelets and multiscale signal processing. Chapman & Hall, Boundary Row, London
Cantero D, Ülker-kaustell M, Karoumi R (2016) Time–frequency analysis of railway bridge response in forced vibration. Mech Syst Signal Process 76–77:518–530
Ülker-kaustell M, Karoumi R (2012) Influence of non-linear stiffness and damping on the train-bridge resonance of a simply supported railway bridge. Eng Struct 41:350–355. https://doi.org/10.1016/j.engstruct.2012.03.060
Teolis A (1998) Computational signal processing with wavelets. Birkhauser
Ribeiro D, Leite J, Meixedo A, Pinto N, Calçada R, Todd M (2021) Statistical methodologies for removing the operational effects from the dynamic responses of a high-rise telecommunications tower. Struct Control Health Monit 28(4):e2700. https://doi.org/10.1002/stc.2700
Yan A, Kerschen G, De BP, Golinval J (2005) Structural damage diagnosis under varying environmental conditions—Part I: a linear analysis. Mech Syst Signal Process 19(4):847–864. https://doi.org/10.1016/j.ymssp.2004.12.002
Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer, New York, pp 112–147
Hastie T, Tibshirani R, Friedman J (2011) The elements of statistical learning, data mining inference, and prediction, 2nd edn. Springer, Stanford, pp 460–462
Santos J, Crémona C, Calado L (2016) Real-time damage detection based on pattern recognition. Struct Concrete 17(3):338–354. https://doi.org/10.1002/suco.201500092
Acknowledgements
This work was financially supported by the Portuguese Foundation for Science and Technology (FCT) through the PhD scholarship SFRH/BD/93201/2013. The authors would like to acknowledge the support of the R&D project RISEN through the H2020|ES|MSC—H2020|Excellence Science|Marie Curie programme, the Portuguese Road and Railway Infrastructure Manager (I.P), the Portuguese National Laboratory for Civil Engineering (LNEC), and the Base Funding—UIDB/04708/2020 of the CONSTRUCT—Instituto de I&D em Estruturas e Construções—financed by national funds through the FCT/MCTES (PIDDAC).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Meixedo, A., Ribeiro, D., Santos, J., Calçada, R., Todd, M.D. (2022). Real-Time Unsupervised Detection of Early Damage in Railway Bridges Using Traffic-Induced Responses. In: Cury, A., Ribeiro, D., Ubertini, F., Todd, M.D. (eds) Structural Health Monitoring Based on Data Science Techniques. Structural Integrity, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-030-81716-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-81716-9_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-81715-2
Online ISBN: 978-3-030-81716-9
eBook Packages: EngineeringEngineering (R0)