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Thermal response separation for bridge long-term monitoring systems using multi-resolution wavelet-based methodologies

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Abstract

This paper presents an application of the multi-resolution wavelet-based methodology to uncover thermal effects from bridge responses based on the distinguished frequency bandwidths. First, periods of daily and seasonal temperature effects are verified using field-measured mid-span deflections of a cable-stayed bridge. Then, the formula to determine the decomposition level is derived on the basis of its capability in dividing the total bandwidth of signals. Moreover, simulated signals on two scales (i.e., minutely and hourly) are applied to validate the effectiveness of the proposed formula. Meanwhile, wavelet basis function ‘coif5′ is selected for thermal response separation due to its more stable performance compared to other functions. In-situ measured mid-span deflections of Xihoumen bridge are taken as an example to validate the effectiveness of the method through the similarity of daily vehicle gross mass and daily mean reconstructed deflection signals (without effects of temperature actions). As a result, the trends of daily mean deflections and vehicle gross mass are generally similar, except for the beginning and end of the time window. In conclusion, it is of practicality to determine the parameters by following the suggestion in this paper when using multi-resolution wavelet-based method to separate temperature responses.

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References

  1. Xu X, Huang Q, Ren Y, Zhao DY, Zhang DY, Sun HB (2019) Condition evaluation of suspension bridges for maintenance, repair and rehabilitation: a comprehensive framework. Struct Infrastruct Eng 15(4):555–567. https://doi.org/10.1080/15732479.2018.1562479

    Article  Google Scholar 

  2. Ko J, Ni YQ (2005) Technology developments in structural health monitoring of large-scale bridges. Eng Struct 27(12):1715–1725. https://doi.org/10.1016/j.engstruct.2005.02.021

    Article  Google Scholar 

  3. Xiong W, Cai CS, Kong B, Zhang XF, Tang PB (2019) Bridge scour identification and field application based on ambient vibration measurements of superstructures. J Mar Sci Eng 7(5):24. https://doi.org/10.3390/jmse7050121

    Article  Google Scholar 

  4. Xu X, Huang Q, Ren Y, Zhao DY, Yang J (2019) Sensor fault diagnosis for bridge monitoring system using similarity of symmetric responses. Smart Struct Syst 23(3):279–293. https://doi.org/10.12989/sss.2019.23.3.279

    Article  Google Scholar 

  5. Huang HB, Yi TH, Li HN (2017) Bayesian combination of weighted principal-component analysis for diagnosing sensor faults in structural monitoring systems. J Eng Mech 143(9):16. https://doi.org/10.1061/(asce)em.1943-7889.0001309

    Article  Google Scholar 

  6. Hua XG, Ni YQ, Chen ZQ, Ko JM (2009) Structural damage detection of cable-stayed bridges using changes in cable forces and model updating. J Struct Eng 135(9):1093–1106. https://doi.org/10.1061/(asce)0733-9445(2009)135:9(1093)

    Article  Google Scholar 

  7. Ren Y, Xu X, Huang Q, Zhao DY, Yang J (2019) Long-term condition evaluation for stay cable systems using dead load-induced cable forces. Adv Struct Eng 22(7):1644–1656. https://doi.org/10.1177/1369433218824486

    Article  Google Scholar 

  8. Xu X, Huang Q, Ren Y, Zhao DY, Yang J, Zhang DY (2019) Modeling and separation of thermal effects from cable-stayed bridge response. J Bridge Eng 24(5):16. https://doi.org/10.1061/(asce)be.1943-5592.0001387

    Article  Google Scholar 

  9. Zhu YJ, Ni YQ, Jin H, Inaudi D, Laory I (2019) A temperature-driven MPCA method for structural anomaly detection. Eng Struct 190:447–458. https://doi.org/10.1016/j.engstruct.2019.04.004

    Article  Google Scholar 

  10. Kromanis R, Kripakaran P (2017) Data-driven approaches for measurement interpretation: analysing integrated thermal and vehicular response in bridge structural health monitoring. Adv Eng Inform 34:46–59. https://doi.org/10.1016/j.aei.2017.09.002

    Article  Google Scholar 

  11. 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):177–189. https://doi.org/10.1177/1475921707081107

    Article  Google Scholar 

  12. Catbas FN, Susoy M, Frangopol DM (2008) Structural health monitoring and reliability estimation: Long span truss bridge application with environmental monitoring data. Eng Struct 30(9):2347–2359. https://doi.org/10.1016/j.engstruct.2008.01.013

    Article  Google Scholar 

  13. Xia Y, Hao H, Zanardo G, Deeks A (2006) Long term vibration monitoring of an RC slab: temperature and humidity effect. Eng Struct 28(3):441–452. https://doi.org/10.1016/j.engstruct.2005.09.001

    Article  Google Scholar 

  14. Zhu YJ, Ni YQ, Jesus A, Liu JL, Laory I (2018) Thermal strain extraction methodologies for bridge structural condition assessment. Smart Mater Struct 27(10):15. https://doi.org/10.1088/1361-665X/aad5fb

    Article  Google Scholar 

  15. Xia Y, Chen B, Zhou XQ, Xu YL (2013) Field monitoring and numerical analysis of Tsing Ma Suspension Bridge temperature behavior. Struct Control Health Monit 20(4):560–575. https://doi.org/10.1002/stc.515

    Article  Google Scholar 

  16. Zhou Y, Sun LM (2019) Insights into temperature effects on structural deformation of a cable-stayed bridge based on structural health monitoring. Struct Health Monit 18(3):778–791. https://doi.org/10.1177/1475921718773954

    Article  Google Scholar 

  17. Kromanis R, Kripakaran P (2014) Predicting thermal response of bridges using regression models derived from measurement histories. Comput Struct 136:64–77. https://doi.org/10.1016/j.compstruc.2014.01.026

    Article  Google Scholar 

  18. Wang H, Zhang YM, Mao JX, Wan HP, Tao TY, Zhu QX (2019) Modeling and forecasting of temperature-induced strain of a long-span bridge using an improved Bayesian dynamic linear model. Eng Struct 192:220–232. https://doi.org/10.1016/j.engstruct.2019.05.006

    Article  Google Scholar 

  19. Zhao D, Ren Y, Huang Q, Xu X (2019) Analysis of temperature-induced deflection of cable-stayed bridge based on BP neural network. IOP Conf Ser Earth Environ Sci 6:062075

    Article  Google Scholar 

  20. Ni YQ, Wang YW, Liao WY, Chen WH (2019) A vision-based system for long-distance remote monitoring of dynamic displacement: experimental verification on a supertall structure. Smart Struct Syst 24(6):769–781. https://doi.org/10.12989/sss.2019.24.6.769

    Article  Google Scholar 

  21. Zhao HW, Ding YL, Nagarajaiah S, Li AQ (2019) Behavior analysis and early warning of girder deflections of a Steel–Truss arch railway bridge under the effects of temperature and trains: case study. J Bridge Eng 24(1):11. https://doi.org/10.1061/(asce)be.1943-5592.0001327

    Article  Google Scholar 

  22. Ding YL, Wang GX, Hong Y, Song YS, Wu LY, Yue Q (2017) Detection and localization of degraded truss members in a steel arch bridge based on correlation between strain and temperature. J Perform Constr Facil 31(5):12. https://doi.org/10.1061/(asce)cf.1943-5509.0001075

    Article  Google Scholar 

  23. Ni YQ, Xia HW, Wong KY, Ko JM (2012) In-service condition assessment of bridge deck using long-term monitoring data of strain response. J Bridge Eng 17(6):876–885. https://doi.org/10.1061/(asce)be.1943-5592.0000321

    Article  Google Scholar 

  24. Wu BJ, Li ZX, Chan THT, Wang Y (2014) Multiscale features and information extraction of online strain for long-span bridges. Smart Struct Syst 14(4):679–697. https://doi.org/10.12989/sss.2014.14.4.679

    Article  Google Scholar 

  25. Wei SY, Zhang ZH, Li SL, Li H (2017) Strain features and condition assessment of orthotropic steel deck cable-supported bridges subjected to vehicle loads using dense FBG strain sensors. Smart Mater Struct. https://doi.org/10.1088/1361-665X/aa7600

    Article  Google Scholar 

  26. Zhou Y, Sun LM, Peng ZJ (2015) Mechanisms of thermally induced deflection of a long-span cable-stayed bridge. Smart Struct Syst 15(3):505–522. https://doi.org/10.12989/sss.2015.15.3.505

    Article  Google Scholar 

  27. Zhou SG, Cheng J (2018) A multi-scale wavelet-based temperature and emissivity separation algorithm for hyperspectral thermal infrared data. Int J Remote Sens 39(22):8092–8112. https://doi.org/10.1080/01431161.2018.1482019

    Article  Google Scholar 

  28. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  Google Scholar 

  29. Meyer Y (1992) Wavelets and operators. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  30. Ahmad MZ, Khan AA, Mezghani S, Perrin E, Mouhoubi K, Bodnar JL, Vrabie V (2016) Wavelet subspace decomposition of thermal infrared images for defect detection in artworks. Infrared Phys Technol 77:325–334. https://doi.org/10.1016/j.infrared.2016.06.018

    Article  Google Scholar 

  31. Vonesch C, Blu T, Unser M (2007) Generalized daubechies wavelet families. IEEE Trans Signal Process 55(9):4415–4429. https://doi.org/10.1109/tsp.2007.896255

    Article  MathSciNet  MATH  Google Scholar 

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Funding

This study was funded by the open project funding of Key Laboratory of Safety and Risk Management on Transport Infrastructure, the Natural Science Foundation of Jiangsu Province (Grant No. BK20181278), Transportation Science Research Project in Jiangsu (Grant No. 2019Z02), and China Postdoctoral Science Foundation (Grant No. 2019M653085).

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Authors and Affiliations

  1. Key Laboratory of Safety and Risk Management on Transport Infrastructure, Ministry of Transport, Nanjing, 210096, Jiangsu, China

    Xiang Xu, Yuan Ren, Qiao Huang & Dan-Yang Zhao

  2. School of Transportation, Southeast University, Nanjing, 210096, Jiangsu, China

    Xiang Xu, Yuan Ren, Qiao Huang & Dan-Yang Zhao

  3. Department of Civil and Environmental Engineering, University of California Irvine, Irvine, CA, 92697, USA

    Yuan Ren

  4. Shenzhen Municipal Design and Research Institute Co., Ltd, Shenzhen, 518029, Guangdong, China

    Zhao-Jie Tong

  5. Zhejiang Zhoushan Bridge Co., Ltd, ZhoushanHangzhou, 316031, China

    Wei-Jie Chang

Authors
  1. Xiang Xu
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  2. Yuan Ren
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  3. Qiao Huang
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  4. Dan-Yang Zhao
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Correspondence to Yuan Ren.

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Xu, X., Ren, Y., Huang, Q. et al. Thermal response separation for bridge long-term monitoring systems using multi-resolution wavelet-based methodologies. J Civil Struct Health Monit 10, 527–541 (2020). https://doi.org/10.1007/s13349-020-00402-7

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  • DOI: https://doi.org/10.1007/s13349-020-00402-7

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