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Temperature-based anomaly diagnosis of truss structure using Markov chain-Monte Carlo method

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Abstract

Considering environmental factors such as temperature in structural health monitoring progress has been a consensus. However, the uncertainty of monitoring data usually makes it difficult. In this paper, the uncertainty factor has been introduced into the anomaly diagnosis process, a Markov chain-Monta Carlo (MCMC) anomaly diagnosis method based on temperature-induced response has been proposed. First, a novel diagnosis index has been developed based on the temperature data and static strain response data collected by the SHM system, the MCMC process is used to analyze the diagnosis index, and the posterior frequency distribution histogram of the actual diagnosis index is obtained. Finally, by analyzing the histogram of an unknown state and the initial state (baseline state) of the structure, the anomaly probability of the unknown condition is obtained, which can be used for anomaly probability diagnosis of components. The availability of the method is evaluated by a laboratory truss structure test under a series of working conditions and is verified by field monitoring data of a hanger roof structure. The results show that the method can make better use of the temperature effect of the structure for anomaly diagnosis, and the uncertainty is well considered.

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References

  1. Xue SD (2020) Recent development and engineering practice of spatial structures in China. Steel Constr (Chinese & English) 35(7):1–16. https://doi.org/10.13206/jgjgSE20041904

    Article  Google Scholar 

  2. Sun H, Di S, Du Z et al (2021) Application of multisynchrosqueezing transform for structural modal parameter identification. J Civ Struct Health Monit 11:1175–1188. https://doi.org/10.1007/s13349-021-00500-0

    Article  Google Scholar 

  3. Ye X, Huang P, Pan C et al (2021) Innovative stabilization diagram for automated structural modal identification based on ERA and hierarchical cluster analysis. J Civ Struct Health Monit 11:1355–1373. https://doi.org/10.1007/s13349-021-00514-8

    Article  Google Scholar 

  4. Sunca F, Ergün M, Altunişik AC et al (2021) Modal identification and fatigue behavior of Eynel steel arch highway bridge with calibrated models. J Civ Struct Health Monit 11:1337–1354. https://doi.org/10.1007/s13349-021-00512-w

    Article  Google Scholar 

  5. Lorenzoni F, De Conto N, da Porto F et al (2019) Ambient and free-vibration tests to improve the quantification and estimation of modal parameters in existing bridges. J Civ Struct Health Monit 9:617–637. https://doi.org/10.1007/s13349-019-00357-4

    Article  Google Scholar 

  6. Cao J, Zhang S, Liu Y (2021) Probabilistic SDDLV method for localizing damage in bridges monitored within one cluster under time-varying environmental temperatures. J Civ Struct Health Monit. https://doi.org/10.1007/s13349-021-00524-6

    Article  Google Scholar 

  7. Sarmadi H, Entezami A, Salar M (2021) Bridge health monitoring in environmental variability by new clustering and threshold estimation methods. J Civ Struct Health Monit 11:629–644. https://doi.org/10.1007/s13349-021-00472-1

    Article  Google Scholar 

  8. Sadhu A, Goli G (2017) Blind source separation-based optimum sensor placement strategy for structures. J Civ Struct Health Monit 7:445–458. https://doi.org/10.1007/s13349-017-0235-6

    Article  Google Scholar 

  9. João PS, Cremona C, André D et al (2015) Static-based early-damage detection using symbolic data analysis and unsupervised learning methods. Front Struct Civ Eng 9(1):1–16. https://doi.org/10.1007/s11709-014-0277-3

    Article  Google Scholar 

  10. Eun HC, Park SY, Lee MS (2013) Static-based damage detection using measured strain and deflection data. Appl Mech Mater 256–259:1097–1100. https://doi.org/10.1007/s11709-014-0277-3

    Article  Google Scholar 

  11. El-Sisi AEDA, El-Husseiny OM, Matar EB et al (2020) Field-testing and numerical simulation of vantage steel bridge. J Civ Struct Health Monit 10:443–456. https://doi.org/10.1007/s13349-020-00396-2

    Article  Google Scholar 

  12. Cocking S, Alexakis H, DeJong M (2021) Distributed dynamic fibre-optic strain monitoring of the behaviour of a skewed masonry arch railway bridge. J Civ Struct Health Monit 11:989–1012. https://doi.org/10.1007/s13349-021-00493-w

    Article  Google Scholar 

  13. Sun F, Hoult NA, Butler LJ et al (2021) Distributed monitoring of rail lateral buckling under axial loading. J Civ Struct Health Monit. https://doi.org/10.1007/s13349-021-00504-w

    Article  Google Scholar 

  14. Han QH, Ma Q, Xu J et al (2021) Structural health monitoring research under varying temperature condition: a review. J Civ Struct Health Monit 11:149–173. https://doi.org/10.1007/s13349-020-00444-x

    Article  Google Scholar 

  15. Duan YF, Li Y, **ang YQ (2011) Strain-temperature correlation analysis of a tied arch bridge using monitoring data. In: 2011 international conference on multimedia technology. IEEE, Piscataway, pp 6025–6028. https://doi.org/10.1109/ICMT.2011.6002979

  16. Alexakis H, Lau FDH, DeJong MJ (2021) Fibre optic sensing of ageing railway infrastructure enhanced with statistical shape analysis. J Civ Struct Health Monit 11:49–67. https://doi.org/10.1007/s13349-020-00437-w

    Article  Google Scholar 

  17. Ding Y, Li AQ (2011) Assessment of bridge expansion joints using long-term displacement measurement under changing environmental conditions. Front Struct Civ Eng 5(3):37–380. https://doi.org/10.1007/s11709-011-0122-x

    Article  Google Scholar 

  18. Baraccani S, Palermo M, Gasparini G (2021) A time domain approach for data interpretation from long-term static monitoring of historical structures. Struct Control Health Monit 28:e2708. https://doi.org/10.1002/stc.2708

    Article  Google Scholar 

  19. **a Q, Zhou LM, Zhang J (2018) Thermal performance analysis of a long-span suspension bridge with long-term monitoring data. J Civ Struct Health Monit 8:543–553. https://doi.org/10.1007/s13349-018-0299-y

    Article  Google Scholar 

  20. **a Q, Zhang J, Tian YD (2017) Experimental study of thermal effects on a long-span suspension bridge. J Bridge Eng 22(7):4017034. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001083

    Article  Google Scholar 

  21. Kulprapha N, Warnitchai P (2012) Structural health monitoring of continuous prestressed concrete bridges using ambient thermal responses. Eng Struct 40:20–38. https://doi.org/10.1016/j.engstruct.2012.02.001

    Article  Google Scholar 

  22. Yarnold MT (2013) Temperature-based structural identification and health monitoring for long-span bridges. Dissertation, Drexel University.

  23. Yarnold MT, Franklin LM, Aktan AE (2015) Temperature-based structural identification of long-span bridges. J Struct Eng 141(11):04015027. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001270

    Article  Google Scholar 

  24. Yarnold MT, Fl M (2015) Temperature-based structural health monitoring baseline for long-span bridges. Eng Struct 86:157–167. https://doi.org/10.1016/j.engstruct.2014.12.042

    Article  Google Scholar 

  25. Murphy B, Yarnold MT (2018) Temperature-driven structural identification of a steel girder bridge with an integral abutment. Eng Struct 155:209–221. https://doi.org/10.1016/j.engstruct.2017.10.074

    Article  Google Scholar 

  26. Lyu M, Zhu X, Yang Q (2017) Connection stiffness identification of historic timber buildings using temperature-based sensitivity analysis. Eng Struct 131:180–191. https://doi.org/10.1016/j.engstruct.2016.11.012

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. Kromanis R (2015) Structural performance evaluation of bridges: characterizing and integrating thermal response. Dissertation, University of Exeter

  29. Kromanis R, Kripakaran P (2016) SHM of bridges: characterising thermal response and detecting anomaly events using a temperature-based measurement interpretation approach. J Civ Struct Health Monit 6(2):237–254. https://doi.org/10.1007/s13349-016-0161-z

    Article  Google Scholar 

  30. Kromanis R, Kripakaran P (2021) Performance of signal processing techniques for anomaly detection using a temperature-based measurement interpretation approach. J Civ Struct Health Monit 11:15–34. https://doi.org/10.1007/s13349-020-00435-y

    Article  Google Scholar 

  31. **a Q, Cheng YY, Zhang J et al (2016) In-service condition assessment of a long-span suspension bridge using temperature-induced strain data. J Bridge Eng 22(3):4016124. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001003

    Article  Google Scholar 

  32. Diao Y, Sui Z, Guo K (2021) Structural damage identification under variable environmental/operational conditions based on singular spectrum analysis and statistical control chart. Struct Control Health Monit 28:e2721. https://doi.org/10.1002/stc.2721

    Article  Google Scholar 

  33. Tu JQ, Tang ZF, Yun CB (2021) Guided wave-based damage assessment on welded steel I-beam under ambient temperature variations. Struct Control Health Monit 28:e2696. https://doi.org/10.1002/stc.2696

    Article  Google Scholar 

  34. Chen DS, Xu WC, Qian HL et al (2020) Effects of non-uniform temperature on closure construction of spatial truss structure. J Build Eng. https://doi.org/10.1016/j.jobe.2020.101532

    Article  Google Scholar 

  35. Xu WC, Chen DS, Qian HL et al (2021) Non-uniform temperature field and effects of large-span spatial truss structure under construction: field monitoring and numerical alanalysis. Struct 29:416–426. https://doi.org/10.1016/j.istruc.2020.11.014

    Article  Google Scholar 

  36. Zhou M, Fan JS, Liu YF et al (2020) Non-uniform temperature field and effect on construction of large-span steel structures. Automat Constr 119:103339. https://doi.org/10.1016/j.autcon.2020.103339

    Article  Google Scholar 

  37. Zhou M, Fan JS, Liu YF et al (2020) Analysis on non-uniform temperature field of steel grids of Bei**g Daxing international airport terminal building core area considering solar radiation. Eng Mech 37(5):46-54/73. https://doi.org/10.6052/j.issn.1000-4750.2019.07.0374 (in Chinese)

    Article  Google Scholar 

  38. Luo YZ, Mei YJ, Shen YB et al (2013) Measurement and analysis of steel structure temperature and stress in National Stadium. J Build Struct 34(11):24–32. https://doi.org/10.14006/j.jzjgxb.2013.11.005 (in Chinese)

    Article  Google Scholar 

  39. Hu YD, Hou RR, **a Q et al (2018) Temperature-induced displacement of supertall structures: a case study. Adv Struct Eng 22(4):982–996. https://doi.org/10.1177/1369433218795288

    Article  Google Scholar 

  40. Sohn H, Dzwonczyk M, Straser EG et al (1999) An experimental study of temperature effect on modal parameters of the Alamosa Canyon Bridge. Earthq Eng Struct Dyn 28(8):879–897. https://doi.org/10.1002/(sici)1096-9845(199908)28:8%3c879::aid-eqe845%3e3.0.co;2-v

    Article  Google Scholar 

  41. **a Q, Tian YD, Cai DX (2020) Structural flexibility identification and fast-Bayesian-based uncertainty quantification of a cable-stayed bridge. Eng Struct 214:110616-1-110616–11. https://doi.org/10.1016/j.engstruct.2020.110616

    Article  Google Scholar 

  42. Xu M, Guo J, Wang S (2021) Structural damage identification with limited modal measurements and ultra-parse Bayesian regression. Struct Control Health Monit 28:e2729. https://doi.org/10.1002/stc.2729

    Article  Google Scholar 

  43. Wang YW, Ni YQ, Zhang QH (2021) Bayesian approaches for evaluating wind-resistant performance of long-span bridges using structural health monitoring data. Struct Control Health Monit 28:e2699. https://doi.org/10.1002/stc.2699

    Article  Google Scholar 

  44. Wang XY, Hou R, **a Y et al (2020) Laplace approximation in sparse Bayesian learning for structural damage detection. Mech Syst Signal Pr 140:106701. https://doi.org/10.1016/j.ymssp.2020.106701

    Article  Google Scholar 

  45. Wang XY, Li L, Beck JL et al (2021) Sparse Bayesian factor analysis for structural damage detection under unknown environmental conditions. Mech Syst Signal Pr 154(11):107563. https://doi.org/10.1016/j.ymssp.2020.107563

    Article  Google Scholar 

  46. Hou R, Wang XY, **a Q et al (2020) Sparse Bayesian learning for structural damage detection under varying temperature conditions. Mech Syst Signal Pr 145:106965. https://doi.org/10.1016/j.ymssp.2020.106965

    Article  Google Scholar 

  47. Wang X, Hou R, **a Y et al (2020) Structural damage detection based on variational Bayesian inference and delayed rejection adaptive Metropolis algorithm. Struct Health Monit 20(4):147592172092125. https://doi.org/10.1177/1475921720921256

    Article  Google Scholar 

  48. Huang T, Schroeder KU (2020) Bayesian probabilistic damage characterization based on a perturbation model using responses at vibration nodes. Mech Syst Signal Pr 139:106444. https://doi.org/10.1016/j.ymssp.2019.106444

    Article  Google Scholar 

  49. Huang T, Schrder KU (2021) IWSHM 2019: Perturbation-based Bayesian damage identification using responses at vibration nodes. Struct Health Monit 20(3):942–959. https://doi.org/10.1177/1475921720985143

    Article  Google Scholar 

  50. Alkam F, Lahmer T (2021) Eigenfrequency-based Bayesian approach for damage identification in catenary poles. Infrastruct. https://doi.org/10.3390/infrastructures6040057

    Article  Google Scholar 

  51. Cantero-Chinchilla S, Malik MK, Chronopoulos D et al (2021) Bayesian damage localization and identification based on a transient wave propagation model for composite beam structures. Compos Struct 267:113849. https://doi.org/10.1016/j.compstruct.2021.113849

    Article  Google Scholar 

  52. Barron RF, Barron BR (2011) Design for thermal stresses. Wiley, New Jersey. https://doi.org/10.1002/9781118093184:416-460

  53. Price R (2003) An essay towards solving a problem in the doctrine of chances. Resonance 8(4):80–88. https://doi.org/10.1007/10.1007/BF02883540

    Article  Google Scholar 

  54. Hastings WK (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57(1):97–109. https://doi.org/10.2307/2334940

    Article  MathSciNet  MATH  Google Scholar 

  55. Metropolis N, Rosenbluth AW, Rosenbluth MN (1953) Equation of state calculations by fast computing machines. J Chem Phys 1(6):1087–1092. https://doi.org/10.1063/1.1699114

    Article  MATH  Google Scholar 

  56. Dutta A, Mckay M, Kopsaftopoulos F et al (2021) Statistical residual-based time series methods for multicopter fault detection and identification. Aerosp Sci Technol 112(1):106649. https://doi.org/10.1016/j.ast.2021.106649

    Article  Google Scholar 

  57. Sarmadi H, Yuen KV (2020) Early damage detection by an innovative unsupervised learning method based on kernel null space and peak-over-threshold. Comput-Aided Civ Inf 36(9):1150–1167. https://doi.org/10.1111/mice.12635

    Article  Google Scholar 

  58. Vidya SR (2018) Verification of the applicability of the Gaussian mixture modelling for damage identification in reinforced concrete structures using acoustic emission testing. J Civ Struct Health Monit 8:395–415. https://doi.org/10.1007/s13349-018-0284-5

    Article  Google Scholar 

  59. Said SE, Dickey DA (1984) Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika 3:599–607. https://doi.org/10.2307/2336570

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The authors would like to thank the support from The Joint Funds of the National Natural Science Foundation of China (U1939208), National Natural Science Foundation of China (No. 51525803) and the 111 Project (B20039).

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Authors and Affiliations

  1. Key Laboratory of Earthquake Engineering Simulation and Seismic Resilience of China Earthquake Administration, Key Laboratory of Coast Civil Structure Safety of Ministry of Education, School of Civil Engineering, Tian** University, Tian**, 300350, China

    Jie Xu & Qinghua Han

  2. School of Civil Engineering, Tian** University, Tian**, 300350, China

    Ming Liu & Qian Ma

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  1. Jie Xu
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  2. Ming Liu
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Correspondence to Qinghua Han.

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Xu, J., Liu, M., Ma, Q. et al. Temperature-based anomaly diagnosis of truss structure using Markov chain-Monte Carlo method. J Civil Struct Health Monit 12, 705–724 (2022). https://doi.org/10.1007/s13349-022-00572-6

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