Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Deflection analysis of long-span girder bridges under vehicle bridge interaction using cellular automaton based traffic microsimulation

1 School of Civil Engineering & Transportation, South China University of Technology, Guangzhou, Guangdong, China
2 College of Civil Engineering, Guangzhou University, Guangzhou, Guangdong, China

Special Issues: Mathematical Methods in Civil Engineering

Deflection is a crucial indicator to reflect the operating condition of girder bridges, which can be used to evaluate structure condition and identify abnormal loading. The paper analyzed the deflection characteristics of long-span girder bridges based on the coupling vibration between stochastic traffic stream and bridge. First, the latest research advances were integrated to form an analytical model of the coupling vibration between stochastic traffic stream and bridge. Then, a generalized Pareto distribution model based on peaks-over-threshold theory was established to predict the extreme girder deflection. Next, a cellular automaton based microsimulation method was proposed to model the traffic loads on bridges, which utilized the intelligent driver car-following model and acceptance distance based lane-changing model. Finally, these theories were applied in the case study of a long-span prestressed concrete continuous girder bridge. It is discovered from the study that, under the coupling vibration between stochastic traffic stream and bridge, the predicted extreme deflection of the case bridge is far lower than the specified design value. Hence, a grading warning model was established and employed to the analysis of deflection monitoring data of the bridge, showing a wide potential prospect of application.
  Article Metrics

Keywords long-span girder bridge; deflection; vehicle bridge interaction; cellular automaton; traffic microsimulation

Citation: Pan Zeng, Ronghui Wang, Zhuo Sun, Junyong Zhou. Deflection analysis of long-span girder bridges under vehicle bridge interaction using cellular automaton based traffic microsimulation. Mathematical Biosciences and Engineering, 2019, 16(5): 5652-5671. doi: 10.3934/mbe.2019281


  • 1. Z. P. Bažant, Q. Yu, G. H. Li, et al., Excessive deflections of record-span prestressed box girder: Lessons learned from the collapse of the Koror-Babeldaob Bridge in Palau, ACI Concrete Int., 32 (2010), 44–52.
  • 2. J. Xie, G. Wang and X. H. Zheng, Review of study of long-term deflection for long span prestressed concrete box-girder bridge, Journal of Highway and Transportation Research and Development (English Edition), 2 (2007), 47–51.
  • 3. Z. P. Bažant, Q. Yu and G. H. Li, Excessive long-time deflections of prestressed box girders. I: Record-span bridge in Palau and other paradigms, J. Struct. Eng., 138 (2012), 676–686.
  • 4. M. Alexander and H. Beushausen, Durability, service life prediction, and modelling for reinforced concrete structures–review and critique, Cement Concrete Res., 122 (2019), 17–29.
  • 5. J. M. Ko and Y. Q. Ni, Technology developments in structural health monitoring of large-scale bridges, Eng. Struct., 27 (2005), 1715–1725.
  • 6. M. Cao, L. Ye, L. Zhou, et al., Sensitivity of fundamental mode shape and static deflection for damage identification in cantilever beams, Mech. Syst. Signal Proc., 25 (2011), 630–643.
  • 7. Y. Zhang, S. T. Lie and Z. Xiang, Damage detection method based on operating deflection shape curvature extracted from dynamic response of a passing vehicle, Mech. Syst. Signal Proc., 35 (2013), 238–254.
  • 8. A. Elhattab, N. Uddin and E. J. O'Brien, Drive-by bridge damage monitoring using bridge displacement profile difference, J. Civil Struct. Health Monit., 6 (2016), 839–850.
  • 9. J. W. Lee, J. D. Kim, C. B. Yun, et al., Health-monitoring method for bridges under ordinary traffic loadings, J. Sound Vib., 257 (2002), 247–264.
  • 10. Z. X. Li, T. H. T. Chan and J. M. Ko, Fatigue damage model for bridge under traffic loading: application made to Tsing Ma Bridge, Theor. Appl. Fract. Mec., 35 (2001), 81–91.
  • 11. B. Enright and E. J. O'Brien, Monte Carlo simulation of extreme traffic loading on short and medium span bridges, Struct. Infrastruct. E., 9 (2013), 1267–1282.
  • 12. C. C. Caprani, E. J. O'Brien and A. Lipari, Long-span bridge traffic loading based on multi-lane traffic micro-simulation, Eng. Struct., 115 (2016), 207–219.
  • 13. X. Ruan, J. Y. Zhou, H. Z. Tu, et al., An improved cellular automaton with axis information for microscopic traffic simulation, Transport. Res. C-Emer., 78 (2017), 63–77.
  • 14. J. Y. Zhou, X. Ruan, X. F. Shi, et al., An efficient approach for traffic load modeling of long span bridges, Struct. Infrastruct. Eng., 15 (2019), 569–581.
  • 15. S. R. Chen and C. S. Cai, Equivalent wheel load approach for slender cable-stayed bridge fatigue assessment under traffic and wind: Feasibility study, J. Bridge Eng., 12 (2007), 755–764.
  • 16. E. J. O'Brien, D. Cantero, B. Enright, et al., Characteristic dynamic increment for extreme traffic loading events on short and medium span highway bridges, Eng. Struct., 32 (2010), 3827–3835.
  • 17. W. S. Han, J. Y. Yan, J. Wu, et al., Analysis of bridge dynamic amplification factors under extra-heavy truck scenarios based on long-term monitoring data, J. Vib. Eng., 27 (2014), 222–232.
  • 18. M. Sekiguchi, E. Ishiwata and Y. Nakata, Dynamics of an ultra-discrete SIR epidemic model with time delay, Math. Biosci. Eng., 15 (2018), 653–666.
  • 19. A. Kesting, M. Treiber and D. Helbing, Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity, Philos. T. R. Soc. A., 368 (2010), 4585–4605.


Reader Comments

your name: *   your email: *  

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved