SpringerLink
Log in

Structural damage detection of 3-D truss structure using nodal response analysis

  • Original Paper
  • Published:
Journal of Civil Structural Health Monitoring Aims and scope Submit manuscript

Abstract

The health monitoring system is considered mandatory during the operating period of truss structures, which are periodically tested to investigate damage detection in the critical components of such structures. Wave propagation-based damage detection has just been implemented in health monitoring systems. This paper proposes four new, efficient, and robust methodologies for systematic structural damage detection of truss structures. The main key used in the proposed methods is the continuous detection of changes in the node position of an element, the velocity time series responses, or the frequency spectrum of the responses affected by probable damage. Maximum amplitude ratio (MAR), Coherency ratio (CR), Maximum amplitude ratio and summation ratio of PSD spectrum (MPSDR & SPSDR) are four approaches for damage detection in the structure, which are based on assigning a relative damage index (RDI) to each truss element and calculating the total damage intensity (TDI) for the entire considered span of the main structure. The proposed methods have been validated both experimentally and mathematically to determine they could be utilized as reliable methods of structural health monitoring. To validate the proposed methods, a laboratory was used to construct a three-dimensional truss structure with two spans. The results show that all methods are able to illustrate the presence of damage in one span of the structure by locating the damaged element that has a higher RDI value. Moreover, the SPSDR method is sensitive to the amount of damage, as the TDI parameter increases efficiently as the stiffness of the damaged element is reduced. The main feature of the proposed methods that distinguishes them from others is their ability to localize and identify the intensity of a 10 percent stiffness reduction in a well-organized element.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Spain)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

Data supporting this study are not publicly available due to legal restrictions. Please contact our research group at http://cee.sutech.ac.ir.

References

  1. Balageas D, Fritzen CP, Guemes A (2006) Structural health monitoring, 1st edn. ISTE Ltd, London

    Book  Google Scholar 

  2. Rens KL, Wipf TJ, Klaiber FW (1997) Review of nondestructive evaluation techniques of civil infrastructure. J Perform Constr Facil 11(4):152–160

    Article  Google Scholar 

  3. Das S, Saha P, Patro SK (2016) Vibration-based damage detection techniques used for health monitoring of structures: a review. J Civ Struct Heal Monit 6(3):477–507

    Article  Google Scholar 

  4. Farrar CR, Worden K (2007) An introduction to structural health monitoring. R Soc Lond Trans Ser A 365(1851):303–315

    Google Scholar 

  5. Chae MJ, Yoo HS, Kim JY, Cho MY (2012) Development of a wireless sensor network system for suspension bridge health monitoring. Autom Constr 21:237–252

    Article  Google Scholar 

  6. Kurata M, Kim J, Zhang Y, Lynch JP, Van Der Linden GW, Jacob V, Thometz E, Hipley P, Sheng LH (2011) Long-term assessment of an autonomous wireless structural health monitoring system at the new carquinez suspension bridge. The Society of Photo-Optical Instrumentation Engineers (SPIE), American Society of Mechanical Engineers, p 7983

  7. Im SB, Hurlebaus S, Kang YJ (2013) Summary review of GPS technology for structural health monitoring. J Struct Eng 139(10):1653–1664

    Article  Google Scholar 

  8. Moaveni B, Hurlebaus S, Moon F (2013) Special issue on real-world applications of structural identification and health monitoring methodologies introduction. J Struct Eng 139(10):1637–1638

    Article  Google Scholar 

  9. Sumitro S, Wang ML (2005) Sustainable structural health monitoring system. Struct Control Health Monit 12(3):445–467

    Article  Google Scholar 

  10. Ji W, Song Y, Liang B (2007) Numeric simulation for structure’s damage identification of space truss. Front Mech Eng China 2:423–428

    Article  Google Scholar 

  11. Weber B, Paultre P (2010) Damage identification in a truss tower by regularized model updating. J Struct Eng: ASCE 136:307–316

    Article  Google Scholar 

  12. Yang QW, ** WM (2009) Damage detection for truss structures using incomplete modes. In: Proceedings of the second international workshop on computer science and engineering (WCSE ’09), Qingdao, China, 28–30 October, 1. IEEE, New York

  13. ** WM, Yang QW, Shen X et al (2013) Damage identification for truss structures using eigenvectors. Adv Mater Res 753:2351–2355

    Article  Google Scholar 

  14. Siriwardane SC (2015) Vibration measurement-based simple technique for damage detection of truss bridges: a case study. Case Stud Eng Fail Anal 4:50–58

    Article  Google Scholar 

  15. Pereira S, Magalhães F, Cunha Á et al (2021) Modal identification of concrete dams under natural excitation. J Civ Struct Health Monit 11:465–484. https://doi.org/10.1007/s13349-020-00462-9

    Article  Google Scholar 

  16. Pölz D, Gfrerer MH, Schanz M (2019) Wave propagation in elastic trusses: an approach via retarded potentials. Wave Motion 87:37–57

    Article  MathSciNet  Google Scholar 

  17. Gao Y, Spencer BF, Bernal D (2007) Experimental verification of the flexibility-based damage locating vector method. ASCE J Eng Mech 133(10):1043–1049

    Article  Google Scholar 

  18. Katebi L, Tehranizadeh M, Mohammad Gholibeyki N (2018) A generalized flexibility matrix-based model updating method for damage detection of plane truss and frame structures. J Civ Struct Health Monit 8:301–314. https://doi.org/10.1007/s13349-018-0276-5

    Article  Google Scholar 

  19. An Y, Blachowski B, Ou JP (2016) A degree of dispersion-based damage localization method. Struct Control Health Monit 23(1):176–192

    Article  Google Scholar 

  20. Marafini F (2023) A proposal of classification for machine-learning vibration-based damage identification methods. Mater Res Proc. https://doi.org/10.21741/9781644902431-96

    Article  Google Scholar 

  21. Singh T, Sehgal S (2022) Damage identification using vibration monitoring techniques. Mater Today: Proc. https://doi.org/10.1016/j.matpr.2022.08.204

    Article  Google Scholar 

  22. Azarfar A, Taal C, Restrepo SE, Liefstingh M (2021) On the interpretation of deep learning models in bearing vibration diagnostics. https://doi.org/10.36001/PHMCONF.2021.V13I1.3047

  23. Avci O, Abdeljaber O, Kiranyaz S, Hussein M, Gabbouj M, Inman DJ (2021) A review of vibration-based damage detection in civil structures : from traditional methods to Machine Learning and Deep Learning applications. Mech Syst Signal Process. https://doi.org/10.1016/J.YMSSP.2020.107077

    Article  Google Scholar 

  24. Akpudo UE, Hur J-W (2021) D-dCNN: a Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics. Energies. https://doi.org/10.3390/EN14175286

    Article  Google Scholar 

  25. Zhu Y, Au S (2017) Spectral characteristics of asynchronous data in operational modal analysis. Struct Control Health Monit 24(11):1–15

    Article  Google Scholar 

  26. Noman AS, Farah D, Ashutosh B (2013) Health monitoring of structures using statistical pattern recognition techniques. J Perform Constr Facil 27(5):575–584

    Article  Google Scholar 

  27. Delautour OR, Omenzetter P (2010) Damage classification and estimation in experimental structures using time series analysis and pattern recognition. Mech Syst Signal Process 24(5):1556–1569

    Article  Google Scholar 

  28. Roy K, Bhattacharya B, Ray-Chaudhuri S (2015) ARX model-based damage sensitive features for structural damage localization using outputonly measurements. J Sound Vib 349:99–122

    Article  Google Scholar 

  29. Daneshvar MH, Gharighoran A, Zareei SA et al (2021) Structural health monitoring using high-dimensional features from time series modeling by innovative hybrid distance-based methods. J Civ Struct Health Monit 11:537–557. https://doi.org/10.1007/s13349-020-00466-5

    Article  Google Scholar 

  30. Goi Y, Kim CW (2017) Damage detection of a truss bridge utilizing a damage indicator from multivariate autoregressive model. J Civ Struct Health Monit 7:153–162. https://doi.org/10.1007/s13349-017-0222-y

    Article  Google Scholar 

  31. Mei Q, Gül M (2016) A fixed-order time series model for damage detection and localization. J Civ Struct Health Monit 6:763–777. https://doi.org/10.1007/s13349-016-0196-1

    Article  Google Scholar 

  32. Lakshmi K, Rao ARM, Gopalakrishnan N (2017) Singular spectrum analysis combined with ARMAX model for structural damage detection. Struct Control Health Monit 24(9):1–21

    Article  Google Scholar 

  33. Jahangiri M, Najafgholipour MA, Dehghan SM, Hadianfard MA (2019) The efficiency of a novel identification method for structural damage assessment using the first vibration mode data. J Sound Vib 458:1–16

    Article  Google Scholar 

  34. Razavi BS, Reza M, Shahrzad M, Razavi S (2021) Damage identification under ambient vibration and unpredictable signal nature. J Civ Struct Health Monit. https://doi.org/10.1007/S13349-021-00503-X

    Article  Google Scholar 

  35. Sohn H, Farrar CR (2001) Damage diagnosis using time series analysis of vibration signals. Smart Mater Struct 10(3):446–451

    Article  Google Scholar 

  36. Gul M, Necati CF (2009) Statistical pattern recognition for structural health monitoring using time series modeling: theory and experimental verifications. Mech Syst Signal Process 23(7):2192–2204

    Article  Google Scholar 

  37. Rongrong H, Yong X (2021) Review on the new development of vibration-based damage identification for civil engineering structures: 2010–2019. J Sound Vib. https://doi.org/10.1016/J.JSV.2020.115741

    Article  Google Scholar 

  38. Nasseri-Moghaddam A (2006) Study of the effect of lateral inhomogeneities on the propagation of Rayleigh waves in an elastic medium. PhD thesis, University of Waterloo

  39. Donattene L, Arnand B, Gilles G (2000) Underground cavity detection: a new method based on seismic rayleigh waves. Eur J Environ Eng Geophys 5:33–53

    Google Scholar 

  40. Phillips C, Cascante G, Hutchinson DJ (2001) Numerical simulation of seismic surface waves. In: Proceedings of the 54th Canadian geotechnical conference, Calgary, Alberta. Canadian Geotechnical Society, pp 1538–1545

  41. Phillips C, Cascante G, Hutchinson DJ (2002) The innovative use of surface waves for void detection and material characterization. In: Proceedings of the symposium on the application of geophysics to engineering and environmental problems, Las Vegas, Nevada, SEG

  42. Phillips C, Nasseri-Moghaddam A, Moore T, Cascante G, Hutchinson DJ (2003) A simple automated method of sasw analysis using multiple receivers. In: Proceedings of the symposium on the application of geophysics to engineering and environmental problems, San Antonio, Texas. SEG, pp 1582–1600

  43. Li J, Hao H, Lo JV (2015) Structural damage identification with power spectral density transmissibility: numerical and experimental studies. Smart Struct Syst 15(1):15–40

    Article  Google Scholar 

  44. Pedram M, Esfandiari A, Shadan F (2014) Finite element model updating using power spectral density of structural response. In: EWSHM - 7th European workshop on structural health monitoring, IFFSTTAR, Inria, Université de Nantes, Jul, Nantes, France

  45. Eun CH, Cho DH, Ahn JY, Lee SG (2015) Damage identification of truss structure using power spectral density estimation. Adv Sci Technol Lett 100:61–66

    Article  Google Scholar 

  46. Bykov AA, Matveenko VP, Shardakov IN, Shestakov AP (2017) Shock wave method for monitoring crack repair processes in reinforced concrete structures. Mech Solids 52:378–383

    Article  Google Scholar 

  47. Shardakov I, Shestakov A, Tsvetkov R, Yepin V, Glot I (2018) Investigation of the influence of cracks on vibration processes in a reinforced concrete structure. Mater Sci Forum 938:132–138

    Article  Google Scholar 

  48. Wei WWS (2006) (2006) Time series analysis: univariate and multivariate methods, 2nd edn. Pearson, Boston, MA

    Google Scholar 

  49. Sharma S (1997) Applied multivariate techniques. Wiley, New York

    Google Scholar 

  50. Welch P (1967) The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust 15(2):70–73

    Article  Google Scholar 

  51. Bathe K-J (1996) Finite element procedures. Prentice-Hall, Upper Saddle River, NJ

    Google Scholar 

  52. Markovic N, Nestorovic T, Stojic D (2015) Numerical modeling of damage detection in concrete beams using piezoelectric patches. Mech Res Commun 64:15–22

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Shiraz University of Technology, Shiraz, Iran

    Reza Bahmanbijari & Hossein Rahnema

Authors
  1. Reza Bahmanbijari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hossein Rahnema
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hossein Rahnema.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bahmanbijari, R., Rahnema, H. Structural damage detection of 3-D truss structure using nodal response analysis. J Civil Struct Health Monit 14, 711–728 (2024). https://doi.org/10.1007/s13349-023-00749-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13349-023-00749-7

Keywords

Search

Navigation