Journal of Civil Structural Health Monitoring

, Volume 8, Issue 4, pp 569–583 | Cite as

Development of a hybrid SHM of cable bridges based on the mixed probability density function

  • Yuhee Kim
  • Jong-chil Park
  • Soobong ShinEmail author
Original Paper


The previous studies on structural health monitoring (SHM) of cable bridges have assessed the health of bridges by a deterministic way or by assuming a calculated health index as a normal distribution. However, various case studies have shown that the health index obtained from field measurement data does not generate a Gaussian distribution due to various factors. In this study, a new mixed probability density function (PDF) is proposed for each local health index based on the findings. The joint PDF for each type of data is determined from the obtained local PDFs. The hybrid index of a bridge is then determined by a combination of local indices. The impact of local damage to the overall health of a bridge is evaluated by taking into account the weight of the sensor type and the weight of the sensor location related to the importance of the member. The proposed hybrid SHM has been verified through a simulation study on a truss structure and by applying it to an existing cable-stayed bridge.


Hybrid SHM Cable bridges Mixed PDF Local index Hybrid index Sensor type Sensor location 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2017R1D1A1B03036080) and INHA University.


  1. 1.
    Ang AH-S, Tang WH (2007) Probability concepts in engineering: emphasis on application to civil and environmental engineering. Wiley, New YorkGoogle Scholar
  2. 2.
    Farrar CR, Baker WE, Bell TM et al (1994) Dynamic characterization and damage detection in the I-40 bridge over the Rio Grande (research report no. LA-12767-MS), Los Alamos National Laboratory (LANL), NMGoogle Scholar
  3. 3.
    Farrar CR, Worden K (2013) Structural health monitoring: a machine learning perspective. Wiley, HobokenGoogle Scholar
  4. 4.
    Figueiredo E, Park G, Figueiras J, Farrar C, Worden K (2009) Structural health monitoring algorithm comparisons using standard data sets (research report no. LA-14393). Los Alamos National Laboratory (LANL), NMCrossRefGoogle Scholar
  5. 5.
    Hellier C (2001) Handbook of nondestructive evaluation. McGraw-Hill, New YorkGoogle Scholar
  6. 6.
    Kim Y (2017) Development and application of reliability-based structural health monitoring algorithm for existing bridges. PhD thesis, Department of Civil Engineering, University of InhaGoogle Scholar
  7. 7.
    Kullaa J (2003) Damage detection of the Z24 bridge using control charts. Mech Syst Signal Process 17(1):163–170CrossRefGoogle Scholar
  8. 8.
    Kullaa J (2009) Eliminating environmental or operational influences in structural health monitoring using the missing data analysis. J Intell Mater Syst Struct 20(11):1381–1390CrossRefGoogle Scholar
  9. 9.
    Li H, Li S, Ou J, Li H (2009) Modal identification of bridges under varying environmental conditions: temperature and wind effects, structural control and health monitoring. Wiley, HobokenGoogle Scholar
  10. 10.
    Ministry of Land, Infrastructure and Transport (2012) Korean road bridge design code 2012 (limit state design). Ministry of Land, Republic of KoreaGoogle Scholar
  11. 11.
    Montgomery DC (2009) Introduction to statistical quality control. Wiley, HobokenzbMATHGoogle Scholar
  12. 12.
    Ni YQ, Zhou HF, Ko JM (2005) Correlating modal properties with temperature using long-term monitoring data and support vector machine technique. Eng Struct 27:1762–1773CrossRefGoogle Scholar
  13. 13.
    Park G, Sohn H, Farrar CR, Inman DJ (2003) Overview of piezoelectric impedance-based health monitoring and path forward. Shock Vib Dig 35(6):451–463CrossRefGoogle Scholar
  14. 14.
    Peeters B, De Roeck G (2000) One year monitoring of the Z24 bridge: environmental influences versus damage effects. Proc. IMAC-XVIII, San Antonio, pp 1570–1576Google Scholar
  15. 15.
    Rytter A (1993) Vibration based inspection of civil engineering structures. Building technology and structural engineering. Aalborg University, AalborgGoogle Scholar
  16. 16.
    Samual PD, Pines DJ (2005) A review of vibration-based techniques for helicopter transmission diagnostics. J Sound Vib 282:475–508CrossRefGoogle Scholar
  17. 17.
    Shull PJ (2002) Nondestructive evaluation: theory, techniques, and applications. Marcel Dekker, New YorkCrossRefGoogle Scholar
  18. 18.
    Shin S, Kim H, Kim Y, Park J-C (2012) Vibration-based BHMS for long-span bridges considering environmental actions. IABMAS 2012:1168–1172Google Scholar
  19. 19.
    Sohn H (2007) Effects of environmental and operational variability on structural health monitoring. Philos Trans R Soc Lond A Math Phys Eng Sci 365(1851):539–560CrossRefGoogle Scholar
  20. 20.
    Wirsching PH, Paez TL, Ortiz K (1995) Random vibrations theory and practice. Wiley, New YorkGoogle Scholar
  21. 21.
    Worden K, Allen DW, Sohn H, Farrar CF (2002) Extreme value statistics for damage detection in mechanical structures (research report no. LA-13905-MS). Los Alamos National Laboratory (LANL), NMGoogle Scholar
  22. 22.
    Worden K, Dulieu-Barton J (2004) An overview of intelligent fault detection in systems and structures. Struct Health Monit 3(1):85CrossRefGoogle Scholar
  23. 23.
    Xu ZD, Wu Z (2007) Simulation of the effect of temperature variation on damage detection in a long-span cable-stayed bridge. Struct Health Monit 6(3):177–189CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringINHA UniversityIncheonKorea
  2. 2.Korea Expressway CorporationHwaseongKorea

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