Bridge health monitoring system based on vibration measurements

  • Evaggelos Ntotsios
  • Costas PapadimitriouEmail author
  • Panagiotis Panetsos
  • Grigorios Karaiskos
  • Kyriakos Perros
  • Philip C. Perdikaris
Original Research Paper


A bridge health monitoring system is presented based on vibration measurements collected from a network of acceleration sensors. Sophisticated structural identification methods, combining information from the sensor network with the theoretical information built into a finite element model for simulating bridge behavior, are incorporated into the system in order to monitor structural condition, track structural changes and identify the location, type and extent of damage. This work starts with a brief overview of the modal and model identification algorithms and software incorporated into the monitoring system and then presents details on a Bayesian inference framework for the identification of the location and the severity of damage using measured modal characteristics. The methodology for damage detection combines the information contained in a set of measurement modal data with the information provided by a family of competitive, parameterized, finite element model classes simulating plausible damage scenarios in the structure. The effectiveness of the damage detection algorithm is demonstrated and validated using simulated modal data from an instrumented R/C bridge of the Egnatia Odos motorway, as well as using experimental vibration data from a laboratory small-scaled bridge section.


Structural health monitoring Model updating Bayesian inference Structural identification Damage detection 


  1. Au SK (2001) On the solution of the first excursion problem by simulation with applications to probabilistic seismic performance assessment. Ph. D. thesis, EERL Report 2001–02, Caltech, Pasadena, CAGoogle Scholar
  2. Beck JL, Au SK (2002) Bayesian updating of structural models and reliability using Markov Chain Monte Carlo simulation. J Eng Mech (ASCE) 128(4): 380–391CrossRefGoogle Scholar
  3. Beck JL, Katafygiotis LS (1998) Updating models and their uncertainties. I: Bayesian statistical framework. J Eng Mech (ASCE) 124(4): 455–461CrossRefGoogle Scholar
  4. Beck JL, Yuen KV (2004) Model selection using response measurements: Bayesian probabilistic approach. J Eng Mech (ASCE) 130(2): 192–203CrossRefGoogle Scholar
  5. Christodoulou K, Papadimitriou C (2007) Structural identification based on optimally weighted modal residuals. Mech Syst Signal Process 21: 4–23CrossRefGoogle Scholar
  6. COMSOL AB (2005) COMSOL multiphysics user’s guide.
  7. Doebling S, Farrar C, Prime M, Shevitz D (1998) Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review. Report LA-13070-MS, Los Alamos National LaboratoryGoogle Scholar
  8. Fritzen CP, Jennewein D, Kiefer T (1998) Damage detection based on model updating methods. Mech Syst Signal Process 12(1): 163–186CrossRefGoogle Scholar
  9. Goursat M, Basseville M, Benveniste A, Mevel L (2000) A Scilab toolbox for output only modal analysis and diagnosis. In: Proceedings of 18th international modal analysis conference, San Antonio, Texas.
  10. Hemez FM, Farhat C (1995) Structural damage detection via a finite element model updating methodology. Int J Analytical Exp Modal Anal 10(3): 152–166Google Scholar
  11. Katafygiotis LS, Cheung SH (2002) MCMC based simulation methodology for reliability calculations. In: Spanos PD, Deodatis G (eds) Fourth international conference on computational stochastic mechanics, Millpress, Rotterdam, the Netherlands, pp 293–299Google Scholar
  12. Katafygiotis LS, Papadimitriou C, Lam HF (1998) A probabilistic approach to structural model updating. Int J Soil Dyn Earthq Eng 17(7–8): 495–507CrossRefGoogle Scholar
  13. Kirkegaard PH, Brincker R (1994) On the optimal locations of sensors for parametric identification of linear structural systems. Mech Syst Signal Process 8: 639–647CrossRefGoogle Scholar
  14. Lam HF, Katafygiotis LS, Mickleborough NC (2004) Application of a statistical model updating approach on phase I of the IASC-ASCE structural health monitoring benchmark study. J Eng Mech (ASCE) 130(1): 34–48CrossRefGoogle Scholar
  15. Lam HF, Ng CT, Veidt M (2007) Experimental characterization of multiple cracks in a cantilever beam utilizing transient vibration data following a probabilistic approach. J Sound Vib 305(1–2): 34–49CrossRefGoogle Scholar
  16. Mottershead JE, Friswell MI (1993) Model updating in structural dynamics: a survey. J Sound Vib 167(2): 347–375CrossRefGoogle Scholar
  17. Ntotsios E, Karakostas Ch, Lekidis V, Panetsos P, Nikolaou I, Papadimitriou C (2008) Structural identification of Egnatia Odos bridges using ambient and earthquake-induced vibrations. Bull Earthq Eng doi: 10.1007/s10518-008-9074-5 Google Scholar
  18. Papadimitriou C (2004) Bayesian inference applied to structural model updating and damage detection. 9th ASCE joint specialty conference on probabilistic mechanics and structural reliability, Albuquerque, New MexicoGoogle Scholar
  19. Papadimitriou C (2005) Pareto optimal sensor locations for structural identification. Comput Methods Appl Mech Eng 194(12–16): 1655–1673CrossRefGoogle Scholar
  20. Papadimitriou C, Katafygiotis LS (2004) Bayesian modeling and updating. In: Nikolaidis N, Ghiocel DM, Singhal S (eds) Engineering design reliability handbook, CRC PressGoogle Scholar
  21. Papadimitriou C, Beck JL, Katafygiotis LS (1997) Asymptotic expansions for reliability and moments of uncertain systems. J Eng Mech (ASCE) 123(12): 1219–1229CrossRefGoogle Scholar
  22. Papadimitriou C, Beck JL, Katafygiotis LS (2001) Updating robust reliability using structural test data. Probab Eng Mech 16(2): 103–113CrossRefGoogle Scholar
  23. Peeters B, Van Den Branden B, Laquiere A, De Roeck G (1999) Output-only modal analysis: development of a GUI for Matlab. In: Proceedings of IMAC 17, Kissimmee, pp 1049–1055Google Scholar
  24. Reynders E, De Roeck G (2007) What’s new in system identification for experimental and operational modal analysis. In: Papadrakakis M, Charmpis DC, Lagaros ND, Tsompanakis Y (eds) ECCOMAS thematic conference on computational methods in structural dynamics and earthquake engineering, Rethymno, 13–16 June 2007Google Scholar
  25. Sohn H, Law KH (1997) Bayesian probabilistic approach for structural damage detection. Earthq Eng Struct Dyn 26: 1259–1281CrossRefGoogle Scholar
  26. Teughels A, De Roeck G (2005) Damage detection and parameter identification by finite element model updating. Arch Comput Methods Eng 12(2): 123–164CrossRefGoogle Scholar
  27. Vanik MW, Beck JL, Au SK (2000) Bayesian probabilistic approach to structural health monitoring. J Eng Mech (ASCE) 126(7): 738–745CrossRefGoogle Scholar
  28. Verboven P, Cauberghe B, Parloo E, Vanlanduit S, Guillaume P (2004) User-assisting tools for a fast frequency-domain modal parameter estimation method. Mech Syst Signal Process 18(4): 759–780CrossRefGoogle Scholar
  29. Yuen KV (2002) Model selection identification and robust control for dynamical systems. Ph.D. thesis, EERL Report 2002–03, Caltech, PasadenaGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Evaggelos Ntotsios
    • 1
  • Costas Papadimitriou
    • 1
    Email author
  • Panagiotis Panetsos
    • 2
  • Grigorios Karaiskos
    • 1
  • Kyriakos Perros
    • 1
  • Philip C. Perdikaris
    • 3
  1. 1.Department of Mechanical and Industrial EngineeringUniversity of ThessalyVolosGreece
  2. 2.Capital Maintenance DepartmentEgnatia Odos S.A.ThermiGreece
  3. 3.Department of Civil EngineeringUniversity of ThessalyVolosGreece

Personalised recommendations