Bridge Structural Identification Using Moving Vehicle Acceleration Measurements

  • Soheil Sadeghi Eshkevari
  • Shamim Pakzad
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


Identification of dynamic characteristics of structures is a desired objective for existing infrastructure and has been accounted as a serious challenge for civil engineers. In this research, a structural identification method is proposed, which is capable of identifying dynamics of structures using sensor data inside vehicles passing over a bridge. The methodology utilizes a special type of identification algorithm facilitated by Expectation Maximization (STRIDEX) that is capable of identifying systems using mobile data networks. In this study, it is assumed that the mobile sensor measurements are the accelerations inside rigid vehicles and are primarily a mixtures of accelerations caused by the road roughness and bridge dynamic acceleration. With this regard, a stochastic State-Space model represents the equation of motion for a linear dynamic vehicle-bridge system consisting of an impure input. The observation vector is treated as a linear mixture of two sources that are not known. Therefore, the problem turns to a Blind Source Separation (BSS) procedure that is aiming to draw out the bridge vibrations from the mixture. An algorithm called Second Order Blind Identification (SOBI) has been utilized for source separation and validated using simulation. The entire algorithm, including both SOBI and STRIDEX acting together, could successfully identify natural frequencies and mode shapes of a numerical bridge model.


Expectation Maximization, Blind Source Separation, System Identification, Output Only Algorithms, Structural Health Monitoring 



Research funding is partially provided by the National Science Foundation through Grant No. CMMI-1351537 by Hazard Mitigation and Structural Engineering program and by a grant from the Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance (PITA).


  1. 1.
    Pakzad, S.N., Fenves, G.L., Kim, S., Culler, D.E.: Design and implementation of scalable wireless sensor network for structural monitoring. J. Inf. Syst. 14(1), 89–101 (2008)Google Scholar
  2. 2.
    Swartz, R.A., Lynch, J.P.: Strategic network utilization in a wireless structural control system for seismically excited structures. J. Struct. Eng. 135(5), 597–608 (2009)CrossRefGoogle Scholar
  3. 3.
    Kim, J., Lynch, J.P.: Experimental analysis of vehiclebridge interaction using a wireless monitoring system and a two-stage system identification technique. Mech. Syst. Signal Process. 28, 3–19 (2012)CrossRefGoogle Scholar
  4. 4.
    Cho, S., et al.: Structural health monitoring of a cable-stayed bridge using wireless smart sensor technology: data analyses. Smart Struct. Syst. 6(5–6), 461–480 (2010)CrossRefGoogle Scholar
  5. 5.
    Dorvash, S., Pakzad, S.N., Cheng, L.: An iterative modal identification algorithm for structural health monitoring using wireless sensor networks. Earthq. Spectra. 29(2), 339–365 (2013)CrossRefGoogle Scholar
  6. 6.
    Sohn, H., Farrar, C.R., Hemez, F.M., Czarnecki, J.J.: A Review of Structural Health Review of Structural Health Monitoring Literature 1996–2001. Los Alamos National Laboratory, Los Alamos, NM (2002)Google Scholar
  7. 7.
    Farrar, C.R.: Structural health monitoring: technological advances to practical implementations. Proc. IEEE. 104(8), 1508–1512 (2016)CrossRefGoogle Scholar
  8. 8.
    Juang, J.-N.: Applied System Identification, 1st edn. Prentice Hall, New York (1994)zbMATHGoogle Scholar
  9. 9.
    Peeters, B., De Roeck, G.: One-year monitoring of the Z 24-bridge: environmental effects versus damage events. Earthq. Eng. Struct. Dyn. 30(2), 149–171 (2001)CrossRefGoogle Scholar
  10. 10.
    Matarazzo, T.J., Pakzad, S.N.: Direct state-space models for time-varying sensor networks. Proc. 10th Int. Structural Health Monitoring 2015. 7, 59–65 (2015)Google Scholar
  11. 11.
    Chang, M., Asce, S.M., Pakzad, S.N., Asce, A.M.: Observer Kalman filter Identification for output-only systems using interactive structural modal identification toolsuite. J. Bridg. Eng. 19, 1–11 (2014)CrossRefGoogle Scholar
  12. 12.
    Chang, M., Pakzad, S.N.: Observer Kalman filter identification for output-only systems using interactive structural modal identification toolsuite. J. Bridg. Eng. 19(5), 4014002 (2013)CrossRefGoogle Scholar
  13. 13.
    Juang, J.-N., Pappa, R.S.: An eigensystem realization algorithm for modal parameter identification and model reduction. J. Guid. 8(5), 620–627 (1985)CrossRefGoogle Scholar
  14. 14.
    Van Overschee, P., De Moor, B.L.: Subspace Identification for Linear Systems: Theory—Implementation—Applications. Springer Science & Business Media, Berlin (2012)zbMATHGoogle Scholar
  15. 15.
    Matarazzo, T.J., Pakzad, S.N.: Scalable structural modal identification using dynamic sensor network data with STRIDEX. Comput. Civ. Infrastruct. Eng. 33(1), 4–20 (2018)CrossRefGoogle Scholar
  16. 16.
    Matarazzo, T.J., Pakzad, S.N.: STRIDE for structural identification using expectation maximization: iterative output-only method for modal identification. J. Eng. Mech. 142(4), (2016)Google Scholar
  17. 17.
    Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Applications, vol. 97. Springer, Cham (2011)CrossRefGoogle Scholar
  18. 18.
    Oja, E., Hyva, A.: Independent component analysis: algorithms and applications. Neural Netw. 13, 411–430 (2000)CrossRefGoogle Scholar
  19. 19.
    Kerschen, G.Ã., Poncelet, F., Golinval, J.: Physical interpretation of independent component analysis in structural dynamics. Mech. Syst. Signal Process. 21, 1561–1575 (2007)CrossRefGoogle Scholar
  20. 20.
    Belouchrani, A., Abed-Meraim, K., Cardoso, J.-F., Moulines, E.: A blind source separation technique using second-order statistics. IEEE Trans. Signal Process. 45(2), 434–444 (1997)CrossRefGoogle Scholar
  21. 21.
    Poncelet, F., Kerschen, G., Golinval, J., Verhelst, D.: Output-only modal analysis using blind source separation techniques. Mech. Syst. Signal Process. 21, 2335–2358 (2007)CrossRefGoogle Scholar
  22. 22.
    Shinozuka, M., Deodatis, G.: Simulation of stochastic processes by spectral representation. Appl. Mech. Rev. 44(4), 191–204 (1991)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Matarazzo, T.J., Pakzad, S.N.: Truncated physical model for dynamic sensor networks with applications in high-resolution mobile sensing and BIGDATA. J. Eng. Mech. 142(5), 4016019 (2016)CrossRefGoogle Scholar
  24. 24.
    González, A., O’brien, E.J., Li, Y.-Y., Cashell, K.: The use of vehicle acceleration measurements to estimate road roughness. Veh. Syst. Dyn. 46(6), 483–499 (2008)CrossRefGoogle Scholar
  25. 25.
    Kong, X., Cai, C.S., Kong, B.: Numerically extracting bridge modal properties from dynamic responses of moving vehicles. J. Eng. Mech. 142(2011), 4016025 (2016)CrossRefGoogle Scholar
  26. 26.
    Huang, D., Wang, T.-L.: Impact analysis of cable-stayed bridges. Comput. Struct. 43(5), 897–908 (1992)CrossRefGoogle Scholar
  27. 27.
    McNeill, S.: Blind Modal Identification (BMID) toolbox. MATLAB (2011)Google Scholar
  28. 28.
    McNeill, S.I., Zimmerman, D.C.: A framework for blind modal identification using joint; approximate diagonalization: Mechanical Systems and Signal Processing. 22(7), 1526–1548 (2008)CrossRefGoogle Scholar
  29. 29.
    Chang, M., Pakzad, S.N.: Optimal sensor placement for modal Identi fi cation of bridge systems considering number of sensing nodes. J. Bridg. Eng. 19(6), 1–10 (2014)CrossRefGoogle Scholar
  30. 30.
    Chang, M., Pakzad, S.N.: Optimal sensor configuration for flexible structures with multi-dimensional mode shapes. Smart Mater. Struct. 24(5), 55012 (2015)CrossRefGoogle Scholar
  31. 31.
    Valeti, B., Matarazzo, T.J., Pakzad, S.N.: Experimental study on wireless mobile sensor configurations for output-only modal identification of a beam testbed. In: Sensors and Instrumentation, vol. 5, pp. 71–77. Springer, Cham (2017)CrossRefGoogle Scholar
  32. 32.
    Matarazzo, T.J., Pakzad, S.N.: Sensitivity metrics for maximum likelihood system identification. ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A Civ. Eng. 2, B4015002 (2015)CrossRefGoogle Scholar

Copyright information

© The Society for Experimental Mechanics, Inc. 2019

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringLehigh UniversityBethlehemUSA

Personalised recommendations