Identification of Frequencies and Track Irregularities of Railway Bridges Using Vehicle Responses: A Recursive Bayesian Kalman Filter Algorithm
Publication: Journal of Engineering Mechanics
Volume 148, Issue 9
Abstract
On-board monitoring of track irregularities and bridge dynamic characteristics based on vehicle vibration responses provides basic data for the condition assessment of high speed railway bridges. However, the identification process inevitably introduces estimation uncertainty because of measurement noise and system parameter uncertainty. Here, in a probability framework, we propose a recursive Bayesian Kalman filtering (RBKF) algorithm for quantifying the identification uncertainty of the track irregularities and bridge natural frequencies. A nonlinear state-space model with measurement noise and process noise was first established for vehicle-bridge (VB) systems. Then the RBKF algorithm was formulated using a nonlinear state-space model, and the identification uncertainty was quantified in terms of estimation variances. A numerical study of two high speed railway bridges validated the RBKF algorithm. This study may help develop new approaches for on-board monitoring and condition assessment of high speed railway bridges.
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Data Availability Statement
All data, models, or code generated or used during the study appear in the published article and are available from the corresponding author by request.
Acknowledgments
The authors are grateful for the financial support from the National Natural Science Foundation of China (Grant No. 51838006) and the Fundamental Research Funds for the Central Universities (Grant No. HUST_2018KFYYX JJ007). The findings and opinions expressed here, however, are those of the authors alone, and are not necessarily the views of the sponsors.
References
Au, S.-K. 2014. “Uncertainty law in ambient modal identification—Part I: Theory.” Mech. Syst. Sig. Process. 48 (1–2): 15–33. https://doi.org/10.1016/j.ymssp.2013.07.016.
Bisht, S. S., and M. P. Singh. 2014. “An adaptive unscented Kalman filter for tracking sudden stiffness changes.” Mech. Syst. Sig. Process. 49 (1–2): 181–195. https://doi.org/10.1016/j.ymssp.2014.04.009.
Cha, Y.-J., J. G. Chen, and O. Büyüköztürk. 2017. “Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters.” Eng. Struct. 132 (Feb): 300–313. https://doi.org/10.1016/j.engstruct.2016.11.038.
Chatzis, M. N., E. N. Chatzi, and S. P. Triantafyllou. 2017. “A discontinuous extended Kalman filter for non-smooth dynamic problems.” Mech. Syst. Sig. Process. 92 (Aug): 13–29. https://doi.org/10.1016/j.ymssp.2017.01.021.
Chen, Y., and M. Q. Feng. 2009. “Structural health monitoring by recursive Bayesian filtering.” J. Eng. Mech. 135 (4): 231–242. https://doi.org/10.1061/(ASCE)0733-9399(2009)135:4(231).
Czop, P., K. Mendrok, and T. Uhl. 2011. “Application of inverse linear parametric models in the identification of rail track irregularities.” Arch. Appl. Mech. 81 (11): 1541–1554. https://doi.org/10.1007/s00419-010-0500-1.
Feng, D., and M. Q. Feng. 2018. “Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection—A review.” Eng. Struct. 156 (Feb): 105–117. https://doi.org/10.1016/j.engstruct.2017.11.018.
Lederman, G., S. Chen, J. H. Garrett, J. Kovačević, H. Y. Noh, and J. Bielak. 2017a. “A data fusion approach for track monitoring from multiple in-service trains.” Mech. Syst. Sig. Process. 95 (Oct): 363–379. https://doi.org/10.1016/j.ymssp.2017.03.023.
Lederman, G., S. Chen, J. H. Garrett, J. Kovačević, H. Y. Noh, and J. Bielak. 2017b. “Track monitoring from the dynamic response of a passing train: A sparse approach.” Mech. Syst. Sig. Process. 90 (Jun): 141–153. https://doi.org/10.1016/j.ymssp.2016.12.009.
Lee, J. S., S. Choi, S.-S. Kim, C. Park, and Y. G. Kim. 2012. “A mixed filtering approach for track condition monitoring using accelerometers on the axle box and bogie.” IEEE Trans. Instrum. Meas. 61 (3): 749–758. https://doi.org/10.1109/TIM.2011.2170377.
Lei, Y., and N. Yang. 2020. “Simultaneous identification of structural time-varying physical parameters and unknown excitations using partial measurements.” Eng. Struct. 214 (Jul): 110672. https://doi.org/10.1016/j.engstruct.2020.110672.
Lei, Y., Y. Zhang, J. Mi, W. Liu, and L. Liu. 2020. “Detecting structural damage under unknown seismic excitation by deep convolutional neural network with wavelet-based transmissibility data.” Struct. Health Monit. 20 (4): 1583–1596. https://doi.org/10.1177/1475921720923081.
Lin, C. W., and Y. B. Yang. 2005. “Use of a passing vehicle to scan the fundamental bridge frequencies: An experimental verification.” Eng. Struct. 27 (13): 1865–1878. https://doi.org/10.1016/j.engstruct.2005.06.016.
Magalhães, F., and L. Cunha. 2011. “Explaining operational modal analysis with data from an arch bridge.” Mech. Syst. Sig. Process. 25 (5): 1431–1450. https://doi.org/10.1016/j.ymssp.2010.08.001.
Real, J., P. Salvador, L. Montalbán, and M. Bueno. 2011. “Determination of rail vertical profile through inertial methods.” J. Rail Rapid Transit 225 (1): 14–23. https://doi.org/10.1243/09544097JRRT353.
Reynders, E., K. Maes, G. Lombaert, and G. D. Roeck. 2015. “Uncertainty quantification in operational modal analysis with stochastic subspace identification: Validation and applications.” Mech. Syst. Sig. Process. 66 (1): 13–30. https://doi.org/10.1016/j.ymssp.2015.04.018.
Särkkä, S. 2013. Bayesian filtering and smoothing. Cambridge, UK: Cambridge University Press.
Tsunashima, H., Y. Naganuma, and T. Kobayashi. 2014. “Track geometry estimation from car-body vibration.” Supplement, Veh. Syst. Dyn. 52 (S1): 207–219. https://doi.org/10.1080/00423114.2014.889836.
Van der Merwe, R. 2004. “Sigma-point Kalman filters for probabilistic inference in dynamic state-space models.” Ph.D. dissertation, OGI School of Science & Engineering, Oregon Health and Science Univ.
Xiao, X., and W.-X. Ren. 2019. “A versatile 3D vehicle-track-bridge element for dynamic analysis of the railway bridges under moving train loads.” Int. J. Struct. Stab. Dyn. 19 (4): 1950050. https://doi.org/10.1142/S0219455419500500.
Xiao, X., W. Shen, and X. He. 2021. “Track irregularity monitoring on high-speed railway viaducts: A novel algorithm with unknown input condensation.” J. Eng. Mech. 147 (6): 04021029. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001928.
Xiao, X., Z. Sun, and W. Shen. 2020. “A Kalman filter algorithm for identifying track irregularities of railway bridges using vehicle dynamic responses.” Mech. Syst. Sig. Process. 138 (Apr): 106582. https://doi.org/10.1016/j.ymssp.2019.106582.
Xiao, X., Y. Yan, and B. Chen. 2019. “Stochastic dynamic analysis for vehicle-track-bridge system based on probability density evolution method.” Eng. Struct. 188 (Jun): 745–761. https://doi.org/10.1016/j.engstruct.2019.02.042.
Yang, N., J. Li, Y. Lei, and H. Hao. 2021. “Identification of time-varying nonlinear structural physical parameters by integrated WMA and UKF/UKF-UI.” Nonlinear Dyn. 106 (1): 681–706. https://doi.org/10.1007/s11071-021-06682-y.
Yang, Y. B., and K. C. Chang. 2009a. “Extraction of bridge frequencies from the dynamic response of a passing vehicle enhanced by the EMD technique.” J. Sound Vib. 322 (4–5): 718–739. https://doi.org/10.1016/j.jsv.2008.11.028.
Yang, Y. B., and K. C. Chang. 2009b. “Extracting the bridge frequencies indirectly from a passing vehicle: Parametric study.” Eng. Struct. 31 (10): 2448–2459. https://doi.org/10.1016/j.engstruct.2009.06.001.
Yang, Y. B., and B. H. Lin. 1995. “Vehicle-bridge interaction analysis by dynamic condensation method.” J. Struct. Eng. 121 (11): 1636–1643. https://doi.org/10.1061/(ASCE)0733-9445(1995)121:11(1636).
Yang, Y. B., Z. L. Wang, K. Shi, H. Xu, and Y. T. Wu. 2020. “State-of-the-art of vehicle-based methods for detecting-various properties of highway bridges and railway tracks.” Int. J. Struct. Stab. Dyn. 20 (13): 2041004. https://doi.org/10.1142/S0219455420410047.
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© 2022 American Society of Civil Engineers.
History
Received: Oct 7, 2021
Accepted: May 3, 2022
Published online: Jul 7, 2022
Published in print: Sep 1, 2022
Discussion open until: Dec 7, 2022
ASCE Technical Topics:
- Algorithms
- Bridge engineering
- Bridge management
- Bridge tests
- Bridge-vehicle interaction
- Bridges
- Bridges (by type)
- Continuum mechanics
- Dynamics (solid mechanics)
- Engineering fundamentals
- Engineering mechanics
- Field tests
- Infrastructure
- Mathematics
- Motion (dynamics)
- Rail transportation
- Railroad bridges
- Railroad tracks
- Railroad trains
- Skew bridges
- Solid mechanics
- Structural engineering
- Tests (by type)
- Transportation engineering
- Uncertainty principles
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