Advertisement

Journal of Civil Structural Health Monitoring

, Volume 9, Issue 5, pp 639–653 | Cite as

Measured properties of structural damping in railway bridges

  • Vincenzo GattulliEmail author
  • Egidio Lofrano
  • Achille Paolone
  • Francesco Potenza
Original Paper
  • 50 Downloads

Abstract

Dissipative properties of a structural system are difficult to be characterized in real structure. Nevertheless, damping features may be dominant in several operating conditions of railway bridges influencing fatigue life or passenger comfort during train passage. Observations treating real data acquired in operational condition on steel and concrete railway bridges belonging to the Italian network permits to highlight dissipative sources and features. Consequently, linearized modal damping ratios are evaluated through a recursive process on the acceleration signals acquired before, during and after train passages and/or in environmental conditions. Stochastic Subspace Identification has been used to identify state-space dynamical models able to reproduce the vibrations. Through these models, characterized by an increasing number of state-space variables, it is possible to extract modal damping ratios. A mechanical interpretation of damping characteristics is pursued through the evaluation of the differences with respect to a classical Rayleigh proportional damping matrix of the viscous matrix belonging to the identified state-space models determined through the system spectral features. A non-proportional damping index is presented as a basis to determine the influence of different sources of non-proportionality in the damping matrix (as the ballast layer under the track) and to justify the high value of damping observed in specific experimental campaigns.

Keywords

Structural damping Dynamic identification Non-proportional damping Railway bridges Experimental results Beam bridges 

Notes

Acknowledgements

The research leading to these results has received funding from the Italian Government under Cipe resolution n.135 (Dec. 21, 2012), project INnovating City Planning through Information and Communication Technologies. The results of the steel bridge are part of a project that has received funding from the Research Fund for Coal and Steel under grant agreement No 800687.

References

  1. 1.
    Imai H, Yun CB, Marujama O, Shinozuca M (1989) Fundamentals of system identification of structures. Probab Eng Mech 51(11):2813–2826Google Scholar
  2. 2.
    Capecchi D, Vestroni F (1993) Identification of finite elements models in structural dynamics. Eng Struct 15(1):21–30CrossRefGoogle Scholar
  3. 3.
    Potenza F, Federici F, Lepidi M, Gattulli V, Graziosi F, Colarieti A (2015) Long-term structural monitoring of the damaged Basilica S. Maria di Collemaggio through a low-cost wireless sensor network. J Civil Struct Health Monit 5(5):655–676CrossRefGoogle Scholar
  4. 4.
    Valvona F, Toti J, Gattulli V, Potenza F (2017) Effective seismic strengthening and monitoring of a masonry vault by using Glass Fiber Reinforced Cementitious matrix with embedded Fiber Bragg Grating sensors. Compos Part B 113:355–370CrossRefGoogle Scholar
  5. 5.
    Gattulli V, Lepidi M, Potenza F, Di Sabatino U (2016) Dynamics of masonry walls connected by a vibrating cable in a historic structure. Meccanica 4(4):2813–2826MathSciNetCrossRefGoogle Scholar
  6. 6.
    Ceci AM, Gattulli V, Potenza F (2013) Serviceability and damage scenario in irregular RC structures: post-earthquake observation and modelling predictions. J Perform Constr Facil 27(1):98–115CrossRefGoogle Scholar
  7. 7.
    Foti D, Gattulli V, Potenza F (2014) Output-only modal identification of a damaged building through rapid dynamic testing for post-earthquake retrofitting interventions. Comput Aided Civil Infrastruct Eng 29(9):659–675CrossRefGoogle Scholar
  8. 8.
    Ko JM, Ni YQ (2005) Technology developments in structural health monitoring of large-scale bridges. Eng Struct 27(12):1715–1725CrossRefGoogle Scholar
  9. 9.
    Romeo F, Lofrano E, Paolone A (2014) Damage identification in a parabolic arch via orthogonal empirical mode decomposition. In: Proceedings of the ASME Design Engineering Technical Conference, 26th Conference on Mechanical Vibration and Noise, Buffalo, New York, USA 17–20, Aug, 2014Google Scholar
  10. 10.
    Brownjohn JMW, Magalhaes F, Caetano E, Cunha A (2010) Ambient vibration re-testing and operational modal analysis of the Humber Bridge. Eng Struct 32(8):2003–2018CrossRefGoogle Scholar
  11. 11.
    Magalhaes F, Caetano E, Cunha A, Flamand O, Grillaud G (2012) Ambient and free vibration tests of the Millau Viaduct: evaluation of alternative processing strategies. Eng Struct 45:372–384CrossRefGoogle Scholar
  12. 12.
    Cross EJ, Koo KY, Brownjohn JMW, Worden K (2013) Long-term monitoring and data analysis of the Tamar Bridge. Mech Syst Signal Process 35:16–34CrossRefGoogle Scholar
  13. 13.
    Peeters B, de Roeck G (2001) Stochastic system identification for operational modal analysis: a review. J Dyn Syst Meas Control 123(4):659–667 (Transictions of the ASME) CrossRefGoogle Scholar
  14. 14.
    Brinker R, Zhang L, Andersen P (2001) Modal identification of output-only systems using frequency domain decomposition. Smart Mater Struct 10(3):441–445CrossRefGoogle Scholar
  15. 15.
    Chen GW, Omenzetter P, Beskhyroun S (2017) Operational modal analysis of an eleven-span concrete bridge subjected to weak ambient excitations. Eng Struct 151:839–860CrossRefGoogle Scholar
  16. 16.
    Kim BH, Lee J, Lee DH (2010) Extracting modal parameters of high-speed railway bridge using TDD technique. Mech Syst Signal Process 24:707–720CrossRefGoogle Scholar
  17. 17.
    Sabemehr A, Lim C, Bagchi A (2018) System identification and model updating of highway bridges using ambient vibration tests. J Civil Struct Health Monit 8:755–771CrossRefGoogle Scholar
  18. 18.
    He XH, Hua XG, Chen ZQ, Huang FL (2011) EMD-based random decrement technique for modal parameter identification of existing railway bridge. Eng Struct 33:1348–1356CrossRefGoogle Scholar
  19. 19.
    Jacobsen NJ, Andersen P, Brinker R (2006) Using enhanced frequency domain decomposition as a robust technique to harmonic excitation in operational modal analysis. In: Proceedings of International Conference on Noise and Vibration Engineering (ISMA 2006), 18–20 Sep 2006Google Scholar
  20. 20.
    Peeters B, van der Auweraer H, Guillame P, Leuridan J (2004) The PolyMAX frequency-domain method: a new standard for modal parameter estimation. Shock Vib 11:395–409CrossRefGoogle Scholar
  21. 21.
    Peeters B, De Roeck G (1999) Reference-based stochastic subspace identification for output-only modal analysis. Mechan Signal Signal Process 13(6):855–878CrossRefGoogle Scholar
  22. 22.
    Siringoringo DM, Fujino Y (2008) System identification of suspension bridge from ambient vibration response. Eng Struct 30(2):462–477CrossRefGoogle Scholar
  23. 23.
    Moreu F, Kim RE, Spencer BFJ (2017) Railroad bridge monitoring using wireless smart sensors. Struct Control Health Monit 24(2):e1863CrossRefGoogle Scholar
  24. 24.
    Li J, Zhu X, Law S-S, Samali B (2019) Indirect bridge modal parameter identification with one stationary and one moving sensors and stochastic subspace identification. J Sound Vib 446:1–21CrossRefGoogle Scholar
  25. 25.
    Giraldo DF, Song W, Dyke SJ, Caicedo JM (2009) Modal identification through ambient vibration: comparative study. J Eng Mech 135(8):759–770CrossRefGoogle Scholar
  26. 26.
    Diaferio M, Foti D, Gentile C, Giannoccaro NI (2015) Dynamic testing of a historical slender building using accelerometers and radar. In: Proceedings of the 6th International Operational Modal Analysis Conference, IOMAC 2015, 12–14 May Gijon, SpainGoogle Scholar
  27. 27.
    Diaferio M, Foti D, Giannoccaro NI (2015) Identification of the modal properties of a building of the Greek heritage. Key Eng Mater 628(2015):150–159Google Scholar
  28. 28.
    Cardoso R, Cury A, Barbosa F (2017) A robust methodology for modal parameters estimation applied to SHM. Mechan Syst Signal Process 95:24–41CrossRefGoogle Scholar
  29. 29.
    Gonzales I, Ulker-Kaustell M, Karoumi R (2013) Seasonal effects on the stiffness properties of a ballasted railway bridge. Eng Struct 57:63–72CrossRefGoogle Scholar
  30. 30.
    Bornet L, Andersson A, Zwolski J, Battini JM (2015) Influence of the ballasted track on the dynamic properties of a truss railway bridge. Struct Infrastruct Eng 11(6):796–803CrossRefGoogle Scholar
  31. 31.
    Gonzales I, Karoumi R (2014) Analysis of the variations in the dynamic behaviour of a ballast bridge using Hilbert transform. Eng Struct 60:126–132CrossRefGoogle Scholar
  32. 32.
    Chen GW, Beskhyroun S, Omenzetter P (2016) Experimental investigation into amplitude-dependent modal properties of an eleven-span motorway bridge. Eng Struct 107:80–100CrossRefGoogle Scholar
  33. 33.
    Somaschini C, Matsuoka K, Collina A (2017) Experimental analysis of a composite bridge under high-speed train passages. Procedia Eng 199:3071–3076CrossRefGoogle Scholar
  34. 34.
    Brunetti M, Ciambella J, Evangelista L, Lofrano E, Paolone A, Vittozzi A (2017) Experimental results in damping evaluation of a high-speed railway bridge. Procedia Eng 199:3015–3020CrossRefGoogle Scholar
  35. 35.
    Castellanos-Toro S, Marmolejo M, Marulanda J, Cruz A, Thomson P (2018) Frequencies and damping ratios of bridges through Operational Modal Analysis using smartphones. Constr Build Mater 188:490–504CrossRefGoogle Scholar
  36. 36.
    Reynders E, Schevenels M, De Roeck G (2014) MACEC 3.3 A Matlab toolbox for experimental and operational modal analysis. Faculty of Engineering, Department of Civil Engineering, Structural Mechanics Section, Kasteelpark Arenberg 40, B-3001 LeuvenGoogle Scholar
  37. 37.
    Zarek JHB, Gibbs BM (1981) The derivation of eigenvalues and mode shapes for the bending motion of a damped beam with general end conditions. J Sound Vib 78(2):185–196CrossRefGoogle Scholar
  38. 38.
    Svedholm C, Zangeneh A, Pacoste C, François S, Karoumi R (2016) Vibration of damped uniform beams with general end conditions under moving loads. Eng Struct 126:40–52CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Structural and Geotechnical EngineeringSapienza University of RomeRomeItaly
  2. 2.Department of Civil, Construction-Architectural and Environmental EngineeringUniversity of L’AquilaL’AquilaItaly

Personalised recommendations