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Tipping point analysis of the NPL footbridge

  • Valerie LivinaEmail author
  • Elena Barton
  • Alistair Forbes
Original Paper

Abstract

The NPL footbridge was used for a pedestrian crossing in the past but now serves as an experimental test ground for assessing structural monitoring sensors, in a regular regime and under various structure-stressing experiments. Algorithms from the tipping point toolbox are applied to study sensor data recorded at the footbridge. Tipping point analysis is an established methodology in environmental sciences, and due to its general approach in studying dynamical systems of arbitrary origin, it can be applied to sensor data as well, which is demonstrated in the present study. Two time series techniques are applied, degenerate fingerprinting and potential analysis, to assess the proximity of critical behaviour in material temperature and tilt records obtained by two sets of sensors in time period 2009–2012. Various detected transitions and bifurcations are reported, and their implications for the structural evolution of the bridge are discussed. The proposed methodology, being computationally fast and easy to implement on a single computer desktop, might be used for real-time asset health monitoring for early warning signals of critical behaviour and for detection of dangerous structural changes. The results of the applied time series techniques characterising the behaviour of the structure are very encouraging. Further collaboration with structural engineers to assess the full-scale dynamics of other bridges and structures is expected.

Keywords

NPL footbridge Tipping point toolbox Potential analysis Early warning indicators 

Notes

Acknowledgments

This work is a part of a research programme undertaken between 2009 and 2012 at National Physical Laboratory (NPL), UK jointly with ITMSOIL, Concrete repairs Ltd, SKM and HA.

References

  1. 1.
    Barton E, Zhang B (2010) Quantitative damage assessment based on environmental bridge response. Federal Highway Administration Conference, New YorkGoogle Scholar
  2. 2.
    Cross EJ, Koo KY, Brownjohn JMW, Worden K (2013) Long-term monitoring and data analysis of the Tamar bridge. Mech Syst Signal Process 35(1–2):16–34CrossRefGoogle Scholar
  3. 3.
    Barton E, Middleton C, Koo K, Crocker L, Brownjohn J (2011) Structural finite element model updating using vibration tests and modal analysis for npl footbridge—SHM demonstrator, DAMAS2011. IOP Conference, OxfordGoogle Scholar
  4. 4.
    Cross EJ, Manson G, Worden K, Pierce SG (2012) Features for damage detection with insensitivity to environmental and operational variations. Philos Trans Royal Soc A 468:4098–4122Google Scholar
  5. 5.
    Brownjohn JMW (2007) Structural health monitoring of civil infrastructure. Philos Trans Royal Soc A 365:589–622CrossRefGoogle Scholar
  6. 6.
    Farrar CR, Lieven NAJ (2007) Damage prognosis: the future of structural health monitoring. Philos Trans Royal Soc A 365:623–632CrossRefGoogle Scholar
  7. 7.
    Yan AM, Kerschen G, De Boe P, Golinval JC (2005) Structural damage diagnosis under varying environmental conditions. Mech Syst Signal Process 19:847–864CrossRefGoogle Scholar
  8. 8.
    Cross EJ, Worden K, Chen Q (2011) Cointegration: a novel approach for the removal of environmental trends in structural health monitoring data. Proc R Soc A 467(2133):2712–2732CrossRefzbMATHGoogle Scholar
  9. 9.
    Meruane V, Heylen W (2012) Structural damage assessment under varying temperature conditions. Struct Health Monit 11(3):345–357CrossRefGoogle Scholar
  10. 10.
    Sohn H (2011) Reference-free crack detection under varying temperature. KSCE J Civ Eng 15(8):1395–1404CrossRefMathSciNetGoogle Scholar
  11. 11.
    Sohn H (2007) Effects of environmental and operational variability on structural health monitoring. Philos Trans Royal Soc A 365:539–560. doi: 10.1098/rsta2006.1935 CrossRefGoogle Scholar
  12. 12.
    Cross EJ, Worden K, Barton E (2012) Damage detection on the NPL footbridge under changing environmental conditions. 6th European Workshop on Structural Health Monitoring, Dresden, 8 ppGoogle Scholar
  13. 13.
    Friswell MI (2007) Damage identification using inverse methods. Philos Trans Royal Soc A 365:393–410CrossRefGoogle Scholar
  14. 14.
    Fassois SD, Sakellariou JS (2007) Time-series methods for fault detection and identification in vibrating structures. Philos Trans Royal Soc A 365:411–448CrossRefMathSciNetGoogle Scholar
  15. 15.
    Hios JD, Fassois SD (2009) Statistical damage detection in smart structure under different temperatures via vibration testing: a global model based approach. Key Eng Mater 413–414:261–268CrossRefGoogle Scholar
  16. 16.
    Staszewski WJ, Robertson AN (2007) Time-frequency and time-scale analysis for structural health monitoring. Philos Trans Royal Soc A 365:449–477CrossRefMathSciNetGoogle Scholar
  17. 17.
    Worden K, Manson G (2007) The application of machine learning to structural health monitoring. Philos Trans Royal Soc A 365:515–537CrossRefGoogle Scholar
  18. 18.
    Livina V, Lenton T (2007) A modified method for detecting incipient bifurcations in a dynamical system. Geophys Res Lett 34:L03712Google Scholar
  19. 19.
    Lenton TM, Myerscough RJ, Marsh R, Livina VN, Price AR, Cox SJ (2009) Using GENIE to study a tipping point in the climate system. Philos Trans Royal Soc A 367(1890):871–884CrossRefzbMATHMathSciNetGoogle Scholar
  20. 20.
    Livina V, Kwasniok F, Lenton T (2010) Potential analysis reveals changing number of climate states during the last 60 kyr. Clim Past 6:77–82CrossRefGoogle Scholar
  21. 21.
    Livina VN, Kwasniok F, Lohmann G, Kantelhardt JW, Lenton TM (2011) Changing climate states and stability: from Pliocene to present. Clim Dyn 37(11–12):2437–2453. doi: 10.1007/s00382-010-0980-2 CrossRefGoogle Scholar
  22. 22.
    Lenton T, Livina V, Dakos V, Scheffer M (2012) Climate bifurcations during the last deglaciation. Clim Past 8:1127–1139. doi: 10.5194/cp-8-1127-2012 CrossRefGoogle Scholar
  23. 23.
    Livina V, Lenton T (2013) A recent tipping point in the Arctic sea-ice cover: abrupt and persistent increase in the seasonal cycle since 2007. Cryosphere 7:275–286. doi: 10.5194/tc-7-275-2013 CrossRefGoogle Scholar
  24. 24.
    Vaz Martins T, Livina V, Majtey AP, Toral R (2010) Resonance induced by repulsive interactions in a model of globally coupled bistable systems. Phys Rev E 81:041103CrossRefGoogle Scholar
  25. 25.
    Dakos V, Carpenter SR, Brock WA, Ellison AM, Guttal V, Ives AR, Kefi S, Livina V, Seekell DA, van Nes EH, Scheffer M (2012) Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS ONE 7(7):e41010. doi: 10.1371/journal.pone.0041010 CrossRefGoogle Scholar
  26. 26.
    Cimatoribus AA, Drijfhout SS, Livina V, van der Schrier G (2013) Dansgaard–Oeschger events: bifurcation points in the climate system. Clim Past 9:323–333. doi: 10.5194/cp-9-323-2013 CrossRefGoogle Scholar
  27. 27.
  28. 28.
  29. 29.
  30. 30.
    Held H, Kleinen T (2004) Detection of climate system bifurcations by degenerate fingerprinting. Geophys Res Lett 31(23):L23207. doi: 10.1029/2004GL020972 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.National Physical LaboratoryMiddlesexUK

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