Skip to main content
SpringerLink
Log in

Examining Methods for Modeling Road Surface Roughness Effects in Vehicle–Bridge Interaction Models via Physical Testing

  • Conference paper
  • First Online:
Dynamics of Civil Structures, Volume 2 (SEM 2023)

Included in the following conference series:

  • 137 Accesses

Abstract

Numerically simulating vehicle–bridge interaction (VBI) models within finite-element models (FEMs) has been a topic of interest over the last two decades. Its applications have been well-established in the structural health monitoring community for extracting the dynamic properties of bridges using instrumented vehicles. Often times, analytically generated surface profiles adopted from renowned standards such as the ISO-8608 are used to simulate road surface conditions and approach a more realistic model. However, previous analytical studies have indicated that current methodologies for modeling the effect of road surface roughness on the vehicle response tend to exaggerate the dynamic response of a vehicle and overshadow bridge frequencies in the vehicle response. To alleviate this issue, several studies have recommended relatively low-amplification roughness factors (\(G_d\)) or have used a moving average filter (MAF) to de-noise analytically generated surface profiles prior to prescribing it into the FEM, but to the authors’ knowledge no experimental investigation has been conducted to validate these recommendations. In this chapter, a full-scale road test is conducted using a passenger vehicle instrumented with accelerometers. A vehicle model is developed using the bicycle concept to approximate the dynamic response of the tested vehicle while including road surface roughness effects. The results are then compared to observe whether the vehicle model contains higher acceleration amplitude values than those of the experiment while using ISO-8608’s \(G_d\) factors for the appropriate road class observed in the test. It is concluded that low \(G_d\) factors, which are commonly used in VBI studies, seem to underestimate the surface roughness effects and thus produce an unrealistic VBI model. Moreover, the use of higher \(G_d\) factors without the implementation of a MAF tends to significantly exaggerate the vehicle’s response. The implementation of a MAF was successful in attenuating high frequency noise while also reducing the acceleration amplitude of the vehicle, but the \(G_d\) value still needed to be relatively low to represent the data from the road testing. Finally, the study concludes with recommendations on how to improve the current approach for modeling road surface roughness in VBI problems and suggestions for future work.

For feedback and support, please see the https://github.com/omarabuodeh1994/surface_roughness.git. No promises are made in terms of timely help from the author.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Yang, Y.B., Chang, K.C.: Extracting the bridge frequencies indirectly from a passing vehicle: parametric study. Eng. Struct. 31(10), 2448–2459 (2009)

    Article  Google Scholar 

  2. Shi, Z., Uddin, N.: Extracting multiple bridge frequencies from test vehicle - a theoretical study. J. Sound Vibrat. 490, 115735 (2021)

    Article  Google Scholar 

  3. Yang, Y.B., Lin, C.W.: Vehicle-bridge interaction dynamics and potential applications. J. Sound Vibrat. 284(1), 205–226 (2005)

    Article  Google Scholar 

  4. Malekjafarian, A., McGetrick, P.J., OBrien, E.J.: A review of indirect bridge monitoring using passing vehicles. Shock Vibrat. 2015, 1–16 (2015)

    Article  Google Scholar 

  5. Yang, Y.-B., Lin, C.W., Yau, J.D.: Extracting bridge frequencies from the dynamic response of a passing vehicle. J. Sound Vibrat. 272(3–5), 471–493 (2004)

    Article  Google Scholar 

  6. Yang, Y.B., Xiong, F., Wang, Z.L., Xu, H.: Extraction of bridge frequencies inclusive of the higher modes by the esmd using the contact-point response. Int. J. Struct. Stabil. Dyn. 20(04), 2050045 (2020)

    Article  MathSciNet  Google Scholar 

  7. Shi, Z., Uddin, N.: Theoretical vehicle bridge interaction model for bridges with non-simply supported boundary conditions. Eng. Struct. 232, 111839 (2021)

    Article  Google Scholar 

  8. Nagayama, T., Reksowardojo, A.P., Su, D., Mizutani, T.: Bridge natural frequency estimation by extracting the common vibration component from the responses of two vehicles. Eng. Struct. 150, 821–829 (2017)

    Article  Google Scholar 

  9. Malekjafarian, A., OBrien, E.J.: On the use of a passing vehicle for the estimation of bridge mode shapes. J. Sound Vibrat. 397, 77–91 (2017)

    Article  Google Scholar 

  10. Jian, X., Xia, Y., Sun, L.: An indirect method for bridge mode shapes identification based on wavelet analysis. Struct. Control Health Monitor. 27(12), e2630 (2020)

    Article  Google Scholar 

  11. He, W.-Y., He, J., Ren, W.-X.: Damage localization of beam structures using mode shape extracted from moving vehicle response. Measurement 121, 276–285 (2018)

    Article  Google Scholar 

  12. Yang, Y.B., Shi, K., Wang, Z.L., Xu, H., Zhang, B., Wu, Y.T.: Using a single-DOF test vehicle to simultaneously retrieve the first few frequencies and damping ratios of the bridge. Int. J. Struct. Stabil. Dyn. 21(08), 2150108 (2021)

    Article  MathSciNet  Google Scholar 

  13. Yang, Y.B., Zhang, B., Chen, Y., Qian, Y., Wu, Y.: Bridge damping identification by vehicle scanning method. Eng. Struct. 183, 637–645 (2019)

    Article  Google Scholar 

  14. Yang, Y.B., Li, Y.C., Chang, K.C.: Effect of road surface roughness on the response of a moving vehicle for identification of bridge frequencies. Int. Multiscale Mech. 5(4), 347–368 (2012)

    Article  Google Scholar 

  15. Eshkevari, S.S., Matarazzo, T.J., Pakzad, S.N.: Bridge modal identification using acceleration measurements within moving vehicles. Mech. Syst. Signal Process. 141, 106733 (2020)

    Article  Google Scholar 

  16. Keenahan, J., OBrien, E.J., McGetrick, P.J., Gonzalez, A.: The use of a dynamic truck–trailer drive-by system to monitor bridge damping. Struct. Health Monitor. 13(2), 143–157 (2014)

    Article  Google Scholar 

  17. ISO-8608. Mechanical Vibration, Road Surface Profiles, Reporting of Measured Data. ISO (2016)

    Google Scholar 

  18. Abuodeh, O., Redmond, L.: A framework for developing efficient vehicle-bridge interaction models within a commercial finite element software. In: Dynamics of Civil Structures, vol. 2, pp. 67–73. Springer International Publishing, Berlin (2023)

    Google Scholar 

  19. Yang, Y.B., Xu, H., Mo, X.Q., Wang, Z.L., Wu, Y.T.: An effective procedure for extracting the first few bridge frequencies from a test vehicle. Acta Mechanica 232(3), 1227–1251 (2021)

    Article  MATH  Google Scholar 

  20. Zhang, L., Zhao, H., OBrien, E. J., Shao, X., Tan, C.: The influence of vehicle–tire contact force area on vehicle–bridge dynamic interaction. Canadian J. Civil Eng. 43(8), 769–772 (2016)

    Article  Google Scholar 

  21. Van Rossum, G., Drake, F.L.: Python 3 Reference Manual. CreateSpace, Scotts Valley (2009)

    Google Scholar 

  22. Carvalho, V.R., Moraes, M.F.D., Braga, A.P., Mendes, E.M.A.M.: Evaluating five different adaptive decomposition methods for EEG signal seizure detection and classification. Biomed. Signal Process. Control 62, 102073 (2020)

    Article  Google Scholar 

  23. Pasca, D.P., Aloisio, A., Rosso, M.M., Sotiropoulos, S.: PyOMA and PyOMA_GUI: A Python module and software for operational modal analysis. SoftwareX 20, 101216 (2022)

    Article  Google Scholar 

  24. Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process. 62(3), 531–544 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  25. Brincker, R., Zhang, L., Andersen, P.: Modal identification of output-only systems using frequency domain decomposition. Smart Mat. Struct. 10(3), 441–445 (2001)

    Article  Google Scholar 

  26. Control systems library for Python. http://github.com/python-control/python-control (2017)

  27. Heißing, B., Ersoy, M.: Chassis Handbook Fundamentals, Driving Dynamics, Components, Mechatronics, Perspectives. Vieweg+Teubner Verlag, Berlin (2011)

    Google Scholar 

  28. Xu, H., Huang, C.C., Wang, Z.L., Shi, K., Wu, Y.T., Yang, Y.B.: Damped test vehicle for scanning bridge frequencies: theory, simulation and experiment. J. Sound Vibrat. 506, 116155 (2021)

    Article  Google Scholar 

  29. AASHTO LRFD Bridge Design Specifications. American Association of State Highway and Transportation Officials (2017)

    Google Scholar 

  30. Sw777rfx wireless professional scale system.

    Google Scholar 

  31. HF sensors: Optical laser height-sensors.

    Google Scholar 

  32. Smith, M.: ABAQUS/Standard User’s Manual, Version 6.14. Dassault Systèmes Simulia Corp, United States (2020)

    Google Scholar 

  33. Jornsen, R.: The Automotive Chassis. Arnold, London (1998)

    Google Scholar 

  34. Virtanen, P., Gommers, R., Oliphant, T.E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S.J., Brett, M., Wilson, J., Jarrod Millman, K., Mayorov, N., Nelson, A.R.J., Jones, E., Kern, R., Larson, E., Carey, C.J., Polat, İ., Feng, Y., Moore, E.W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E.A., Harris, C.R., Archibald, A.M., Ribeiro, A.H., Pedregosa, F., van Mulbregt, P.: SciPy 1.0 Contributors. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020)

    Google Scholar 

  35. Wang, Z., He, G., Du, W., Zhou, J., Han, X., Wang, J., He, H., Guo, X., Wang, J., Kou, Y.: Application of parameter optimized variational mode decomposition method in fault diagnosis of gearbox. IEEE Access 7, 44871–44882 (2019)

    Article  Google Scholar 

  36. Welch, P.: The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70–73 (1967)

    Article  Google Scholar 

  37. Garcia-Pozuelo, D., Gauchia, A., Olmeda, E., Diaz, V.: Bump modeling and vehicle vertical dynamics prediction. Adv. Mech. Eng. 6, 736576 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Glenn Department of Civil Engineering, Clemson University, Clemson, SC, USA

    Omar Abuodeh

  2. Exponent, Atlanta, GA, USA

    William Locke

  3. Glenn Department of Civil Engineering, Clemson University, Clemson, SC, USA

    Laura Redmond

  4. Department of Mechanical Engineering, Clemson University, Clemson, SC, USA

    Laura Redmond

  5. Department of Automotive Engineering, Clemson University, Greenville, SC, USA

    Rakesh Vulchi Sreenivasulu & Matthias Schmid

Authors
  1. Omar Abuodeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. William Locke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laura Redmond
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rakesh Vulchi Sreenivasulu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matthias Schmid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Omar Abuodeh .

Editor information

Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Hae Young Noh

  2. University of North Carolina at Charlotte, Charlotte, NC, USA

    Matthew Whelan

  3. University of Oklahoma, Norman, OK, USA

    P. Scott Harvey

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Society for Experimental Mechanics, Inc.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Abuodeh, O., Locke, W., Redmond, L., Sreenivasulu, R.V., Schmid, M. (2024). Examining Methods for Modeling Road Surface Roughness Effects in Vehicle–Bridge Interaction Models via Physical Testing. In: Noh, H.Y., Whelan, M., Harvey, P.S. (eds) Dynamics of Civil Structures, Volume 2. SEM 2023. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-031-36663-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-36663-5_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36662-8

  • Online ISBN: 978-3-031-36663-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation