Skip to main content
SpringerLink
Log in

Proposing a novel structural damage indicator for beams: integrating single-node mode shape parameters and additional mass methods

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

In order to solve the problem of structural damage identification, this paper proposes a damage identification method, called ASS, based on a single-node mode shape parameter. The method, which combines the mode shape parameter and the additional mass methods, can accurately identify the structural damage position and the damage parameter. Moreover, the best accuracy is achieved when an additional mass of approximately 5% of the total mass of the beam is used near the fixed hinge support. In the proposed identification method, only the single-node mode shape parameter of the beam structure is required to judge the position of the damage. This can lead to its straightforward adoption in practical engineering situations requiring structural damage detection in beams.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

Not applicable.

Code availability

Not applicable.

References

  1. Doebling SW, Farrar CR, Prime MB (1998) A summary review of vibration-based damage identification methods. Shock Vibr Dig 30(2):91–105

    Article  Google Scholar 

  2. QiuWei Y (2008) Two-stage approach for structural damage diagnosis using incomplete modal parameters. J Mech Strength 30(4):555–558

    Google Scholar 

  3. Xin S (2014) Study on bridge damage identification based on dynamic detection [D]. Chang’an University

    Google Scholar 

  4. Pandey AK, Biswas M (1994) Damage detection in structures using changes in flexibility. J Sound Vib 169(1):3–17

    Article  Google Scholar 

  5. Lee YS, Chung MJ (2000) A study on crack detection using eigenfrequency test data. Comput Struct 77(3):327–342

    Article  Google Scholar 

  6. Pandey AK, Biswas M, Samman MM (1991) Damage detection from changes in curvature mode shapes. J Sound Vib 145(2):321–332

    Article  Google Scholar 

  7. Songtao X, Yuyin Q, Rong C et al (2003) Damage identification and experiments of frame structure base on second-order frequency sensitivity. J Tongji Univ 31(3):263–267

    Google Scholar 

  8. Xiaochao C, Qibo M (2014) Damage detection for cracked beams by using auxiliary mass approach combined with fractal dimension method. Noise Vib Control 34(3):25–30

  9. Zhisong W, Huiyong G, Zhengliang L (2008) Identification methods for different structural damage based on frequency response. Eng Mech 6:146–151

  10. Zhong S, Oyadiji SO (2008) Identification of cracks in beams with auxiliary mass spatial probing by stationary wavelet transform. J Vib Acoust 130(4):1257–1261

    Article  Google Scholar 

  11. Xuesong L, Hongwei M, Yizhou L (2019) Structural damage identification based on convolution neural network. J Vib Shock 38(01):167–175

    Google Scholar 

  12. Dongbing Z, Xianglin J, Guoliang Z (2012) Study on influence of the truss bridge’s nature frequency by added massed. J Wuhan Univ Technol 34(3):97–100

    Google Scholar 

  13. Shuiping F (2007) Study of bridge damage diagnosis based on modal analysis theory. Nanchang University

    Google Scholar 

  14. Nobukawa S, Nishimura H, Yamanishi T (2019) Pattern classification by spiking neural networks combining self-organized and reward-related spike-timing-dependent plasticity. J Artif Intell Soft Comput Res 4:283–291

  15. Lee J, Kramer BM (1993) Analysis of machine degradation using a neural network based pattern discrimination model. J Manuf Syst 12(5):379–387

  16. Mask G, Wu X, Ling K An improved model for gas-liquid flow pattern prediction based on machine learning. J Pet Sci Eng 183:106370

  17. Ying W, Yong F, Priyanka B, Christos D (2010) High-dimensional pattern regression using machine learning: from medical images to continuous clinical variables. NeuroImage 50(4):1519–1535

  18. Thach NN, Ngo DB-T, Xuan-Canh P, Hong-Thi N, Thi BH, Nhat-Duc H, Dieu TB (2018) Spatial pattern assessment of tropical forest fire danger at Thuan Chau area (Vietnam) using GIS-based advanced machine learning algorithms: a comparative study. Ecol Inform 46:74–85

  19. Zanoni M, Fontana FA, Stella F (2015) On applying machine learning techniques for design pattern detection. J Syst Softw 103:102–117

  20. Tran NM, Burdejová P, Ospienko M, Härdle WK (2019) Principal component analysis in an asymmetric norm. J Multivar Anal 171:1–21

  21. McKeown MJ, Hansen LK, Sejnowsk TJ (2003) Independent component analysis of functional MRI: what is signal and what is noise? Curr Opin Neurobiol 13(5):620–629

  22. Vigário R, Särelä J, Jousmäki V, Hämäläinen M, Oja E (2000) Independent component approach to the analysis of EEG and MEG recordings. IEEE Trans Biomed Eng 47(5):589–593

  23. Safavi H, Correa N, Xiong W, Roy A, Adali T, Korostyshevskiy VR, Whisnant CC, Seillier-Moiseiwitsch F (2008) Independent component analysis of 2-D electrophoresis gels. Electrophor 29(19):4017–4026

  24. Ekezie DD (2013) Principal component analysis, an aid to interpretation of data. A case study of oil palm (Elaeis guineensis Jacq.). J Emerg Trends Eng Appl Sci 4(2):237–241

  25. Ma QL, Yan A, Hu Z, Li Z, Fan B (2000) Principal component analysis and artificial neural networks applied to the classification of Chinese pottery of neolithic age. Anal Chim Acta 406(2):247–256

  26. Almeida MR, Logrado LPL, Zacca JJ, Correa DN, Poppi RJ (2017) Raman hyperspectral imaging in conjunction with independent component analysis as a forensic tool for explosive analysis: the case of an ATM explosion. Talanta 174:628–632

  27. Ludwig A, Pustal B, Herlach DM (2001) General concept for a stability analysis of a planar interface under rapid solidification conditions in multi-component alloy systems. Mater Sci Eng A 304:277–280

  28. Dutta RK, Roijers RB, Mutsaers PHA, de Goeij JJM, van der Vusse GJ (2005) Principal component analysis of elements in atherosclerotic human coronary arteries. Nucl Instrum Methods Phys Res B 231(1-4):245–250

Download references

Funding

This work was supported by Shaanxi Institute of Technology project (Gfy23-23).

Author information

Authors and Affiliations

  1. School of Architecture and Thermal Engineering, Shaanxi Institute of Technology, Xian, 710300, Shaanxi, China

    Hu Sun & Zhuyao Du

Authors
  1. Hu Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhuyao Du
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hu Sun.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

The authors claim that none of the contents in this manuscript has been published or considered for publication elsewhere.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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

Sun, H., Du, Z. Proposing a novel structural damage indicator for beams: integrating single-node mode shape parameters and additional mass methods. Int J Adv Manuf Technol (2024). https://doi.org/10.1007/s00170-023-12893-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00170-023-12893-x

Keywords

Search

Navigation