Skip to main content
SpringerLink
Log in

Damage Detection in Steel Beams Using Generalized Flexibility Quotient Difference Based Damage Index and Artificial Neural Network

  • Original Paper
  • Published:
Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Background

Failure of the structure may be avoided if, the damage is noticed at an early stage and proper remedial work is performed. In this context, this paper presents a detection of structural damage in steel beams, which are major load carrying members in bridges and buildings.

Method

In the present study, the Generalized Flexibility Energy Quotient Difference Index (χ) is determined at various locations of the beam by introducing the damage by means of a cut by 0.5 mm to 2.5 mm at an interval of 0.25 mm. In practice, it is very difficult to capture all the modes of vibration. Hence, the method followed uses the first mode of vibration only for quantifying the magnitude of damage and its location. The validation is done between numerical simulations and experimental works for the estimation of first frequency. The efficiency of the method is tested by inducing the damage very close to the support. In ANN, the depth of cut at various locations of the beam and χ is utilized in the input layer.

Results

The predicted depth of cut or severity of damage from the output layer of ANN model is compared with the actual depth of cut induced in the beam. Finally, the polynomial equations are formulated for the damaged elements, which are the major contribution of the present study. These equations can be used as a ready reckoner to estimate the actual depth of cut in the beam. This approach is validated through a beam with fixed ends and two-span beam, and the results indicate that this method is very much beneficial in preventing the structural failure.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Nick H, Aziminejad A, Hosseini MH, Laknejadi K (2021) Damage identification in steel girder bridges using modal strain energy-based damage index method and artificial neural network. Eng Fail Anal 119:105010. https://doi.org/10.1016/j.engfailanal.2020.105010

    Article  Google Scholar 

  2. Sadeghi F, Yang Y, Zhu X, Li J (2021) Damage identification of steel-concrete composite beams based on modal strain energy changes through general regression neural network. Eng Struct 244:112824. https://doi.org/10.1016/j.engstruct.2021.112824

    Article  Google Scholar 

  3. Liu H, Song G, Jiao Y, Zhang P, Wang X (2014) Damage identification of bridge based on modal flexibility and neural network improved by particle swarm optimization. Math probl Eng 2014:640925. https://doi.org/10.1155/2014/640925

    Article  Google Scholar 

  4. Tran-Ngoc H, Khatir S, De Roeck G, Bui-Tien T, WahabM A (2019) An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm. Eng Struct 199:109637. https://doi.org/10.1016/j.engstruct.2019.109637

    Article  Google Scholar 

  5. Kao CY, Hung S-L (2003) Detection of structural damage via free vibration responses generated by approximating artificial neural networks. Comput Struct 81:2631–2644. https://doi.org/10.1016/S0045-7949(03)00323-7

    Article  Google Scholar 

  6. Al-Athel KS, Alhasan MM, Alomari AS, Abdul FazalArif M (2022) Damage characterization of embedded defects in composites using a hybrid thermography, computational, and artificial neural networks approach. Heliyon 8(8):e10063. https://doi.org/10.1016/j.heliyon.2022.e10063

    Article  PubMed  PubMed Central  Google Scholar 

  7. Movsessian A, Cava DG, Tcherniak D (2021) An artificial neural network methodology for damage detection: demonstration on an operating wind turbine blade. Mech Syst Signal Process. https://doi.org/10.1016/j.ymssp.2021.107766

    Article  Google Scholar 

  8. Chathurangi M, Raniligama SM, Thambiratham DP, Chan THT, Fawzia S (2021) Damage assessment in hyperbolic cooling towers using mode shape curvature and artificial neural networks. Eng Fail Anal 129:105728. https://doi.org/10.1016/j.engfailanal.2021.105728

    Article  Google Scholar 

  9. Padil KH, Bakhary N, Abdulkareem M, Li J, Hao H (2020) Non-probabilistic method to consider uncertainties in frequency response function for vibration-based damage detection using Artificial Neural Network. J Sound Vib 467:115069. https://doi.org/10.1016/j.jsv.2019.115069

    Article  Google Scholar 

  10. Khatir S, Tiachacht S, Le Thanh C, Ghandourah E, Mirjalili S, Wahab MA (2021) An improved artificial neural network using arithmetic optimization algorithm for damage assessment in FGM composite plates. Compos Struct 273:114287. https://doi.org/10.1016/j.compstruct.2021.114287

    Article  CAS  Google Scholar 

  11. Shu J, Zhang Z, Gonzalez I, Karoumi R (2013) The application of a damage detection method using artificial neural network and train-induced vibrations on a simplified railway bridge model. Eng Struct 52:408–421. https://doi.org/10.1016/j.engstruct.2013.02.031

    Article  Google Scholar 

  12. Hakim S, Abdul Razak H (2012) Damage detection of steel bridge girder using artificial neural networks. In: Paipetis A, Matikas T, Aggelis D, Van Hemelrijck D (eds) Emerging technologies in non-destructive testing. CRC Press, USA, pp 409–414

    Chapter  Google Scholar 

  13. Mehrjoo M, Khaji N, Moharrami H, Bahreininejad A (2008) Damage detection of truss bridge joints using neural networks. Expert Syst Appl 35:1122–1131

    Article  Google Scholar 

  14. Rosales MB, Filipich CP, Buezas FS (2009) Crack detection in beam like structures. Eng Struct 31:2257–2264. https://doi.org/10.1016/j.engstruct.2009.04.007

    Article  Google Scholar 

  15. Sha G, Tadzienski M, Cao M, Ostachowicz W (2019) A novel method for single and multiple damage detection in beams using relative natural frequency changes. Mech Syst Signal Process 132:335–352. https://doi.org/10.1016/j.ymssp.2019.06.027

    Article  ADS  Google Scholar 

  16. Shifrin EI, Lebedev IM (2020) Identification of multiple cracks in a beam by natural frequencies. Eur J Mech A Solids 84:104076. https://doi.org/10.1016/j.euromechsol.2020.104076

    Article  MathSciNet  Google Scholar 

  17. He K, Zhu WD (2011) A vibration-based structural damage detection method and its applications to engineering structures. Int J Smart Nano Mater 2(3):194–218. https://doi.org/10.1080/19475411.2011.594105

    Article  Google Scholar 

  18. Manoach E, Trendafilova I (2008) Large amplitude vibrations and damage detection of rectangular plates. J Sound Vib 315:591–606. https://doi.org/10.1016/j.jsv.2008.02.016

    Article  ADS  Google Scholar 

  19. Zou Y, Tong L, Steven GP (2000) Vibration-based model-dependent damage (delamination) identification and health monitoring for composite structures a review. J Sound Vib 230(2):357–378. https://doi.org/10.1006/jsvi.1999.2624

    Article  ADS  Google Scholar 

  20. Jiang Y, Wang N, Zhong Y (2021) A two-step damage quantification method for beam structures. Measurement 168:108434. https://doi.org/10.1016/j.measurement.2020.108434

    Article  Google Scholar 

  21. Hanumanthappa S (2022) A new structural damage detection method for cantilever beam using generalized flexibility quotient difference method. J Vib Eng Technol. https://doi.org/10.1007/s42417-022-00655-0

    Article  Google Scholar 

  22. Li J, Baisheng Wu, Zeng QC, Lim CW (2010) A generalized flexibility matrix based approach for structural damage location. J Sound Vib 329:4583–4587. https://doi.org/10.1016/j.jsv.2010.05.024

    Article  ADS  Google Scholar 

  23. Masoumi M, Jamshidi E, Bamdad M (2015) Application of generalized flexibility matrix in damage identification using imperialist competitive algorithm. KSCE J Civ Eng 19:994–1001. https://doi.org/10.1007/s12205-015-0224-4

    Article  Google Scholar 

  24. Gomes GF, Alves F, de Almeida D, Junqueira M, Simoes S, da Cunha A, Ancelotti C (2019) Optimized damage identification in CFRP plates by reduced mode shapes and GA-ANN methods. Eng Struct 181:111–123. https://doi.org/10.1016/j.engstruct.2018.11.081

    Article  Google Scholar 

  25. Li Z-X, Yang X-M (2008) Damage identification for beams using ANN based on statistical property of structural responses. Comput Struct 86:64–71. https://doi.org/10.1016/j.compstruc.2007.05.034

    Article  ADS  Google Scholar 

  26. Shahsavari V, Chouinard L, Bastien J (2017) Wavelet-based analysis of mode shapes for statistical damage detection and localization of damage in beams using likelihood ratio test. Eng Struct 132:494–507. https://doi.org/10.1016/j.engstruct.2016.11.056

    Article  Google Scholar 

  27. Mousavi AA, Zhang C, Masri SF, Gholipour G (2021) Damage detection and localization of a steel truss bridge model subjected to impact and white noise excitations using empirical wavelet transform neural network approach. Measurement 185:110060. https://doi.org/10.1016/j.measurement.2021.110060

    Article  Google Scholar 

  28. Corbally R, Malekjafarian A (2022) A data-driven approach for drive-by damage detection in bridges considering the influence of temperature change. Eng Struct 253:113783. https://doi.org/10.1016/j.engstruct.2021.113783

    Article  Google Scholar 

  29. Yi-Chun D, Stephanus A (2018) Levenberg-marquardt neural network algorithm for degree of arteriovenous fistula stenosis classification using a dual optical photoplethysmography sensor. Sensors 18:1–18. https://doi.org/10.3390/s18072322

    Article  Google Scholar 

  30. Ozyildirim BM, Kiran M (2021) Levenberg-Marquardt multi-classification using hinge loss function. Neural Netw 143:564–571. https://doi.org/10.1016/j.neunet.2021.07.010

    Article  PubMed  Google Scholar 

  31. Dackermann U (2009) Vibration-based damage identification methods for civil engineering structures using artificial neural networks. https://opus.lib.uts.edu.au/handle/10453/20303

  32. Dahak M, Touat N, Benseddiq N (2017) On the classification of normalized natural frequencies for damage detection in cantilever beam. J Sound Vib 402:70–84. https://doi.org/10.1016/j.jsv.2017.05.007

    Article  ADS  Google Scholar 

  33. Pai PF, Jin S (2000) Locating structural damage by detecting boundary effects. J Sound Vib 231(4):1079–1110. https://doi.org/10.1006/jsvi.1999.2654

    Article  ADS  Google Scholar 

  34. Zhou X, Sun Y, Duan M, Li W (2020) Numerical investigation on crack identification using natural frequencies and mode shapes of a drilling riser during deployment and retrieval. J Pet Sci Eng 195:107721. https://doi.org/10.1016/j.petrol.2020.107721

    Article  CAS  Google Scholar 

  35. Shumon Mia Md, Shahidul Islam Md, Ghosh U (2017) Modal analysis of cracked cantilever beam by finite element simulation. Proced Eng 194:509–516. https://doi.org/10.1016/j.proeng.2017.08.178

    Article  Google Scholar 

  36. Park HW, Lim T (2018) Investigating a common premise in structural health monitoring: are higher modal frequencies more sensitive to an incipient crack on a beam than lower ones? Eng Struct 176:385–395. https://doi.org/10.1016/j.engstruct.2018.08.102

    Article  Google Scholar 

  37. Fan W, Qiao P (2011) Vibration-based damage identification methods: a review and comparative study. Struct Health Monitor Int J 10(1):83–111. https://doi.org/10.1177/1475921710365419

    Article  Google Scholar 

  38. Satpute D, Baviskar P, Gandhi P, Chavanke M, Aher T (2017) Crack detection in cantilever shaft beam using natural frequency. Mater Today Proceed 4:1366–1374. https://doi.org/10.1016/j.matpr.2017.01.158

    Article  Google Scholar 

  39. Kasinos S, Palmeri A, Lombardo M (2015) Using the vibration envelope as a damage-sensitive feature in composite beam structures. Structures 1:67–75. https://doi.org/10.1016/j.istruc.2014.10.001

    Article  Google Scholar 

  40. Kim MJ, Eun HC (2017) Identification of damage-expected members of truss structures using frequency response function. Adv Mech Eng 9(1):1–10. https://doi.org/10.1177/1687814016687911

    Article  Google Scholar 

  41. Das S, Saha P (2018) A review of some advanced sensors used for health diagnosis of civil engineering structures. Measurement 129:68–90. https://doi.org/10.1016/j.measurement.2018.07.008

    Article  ADS  Google Scholar 

  42. Chen B, Nagarajaiah S (2013) Flexibility-based structural damage identification using Gauss-Newton method. Sadhana 38(4):557–569. https://doi.org/10.1007/s12046-013-0151-3

    Article  MathSciNet  CAS  Google Scholar 

  43. Nakasone Y, Yoshimoto S, Stolarski TA (2006) Engineering analysis with ANSYS software. Elsevier, Cornwall

    Google Scholar 

  44. Balaji R Sharma (2010) Feasibility of use of four-post road simulator for automotive modal applications. Master of Science thesis, University of Cincinnatti. https://etd.ohiolink.edu/apexprod/rws_etd/send_file/send?accession=ucin1277133229&disposition=inline

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Siddaganga Institute of Technology, B.H. Road, Tumakuru, Karnataka, 572 103, India

    Siddesha Hanumanthappa

Authors
  1. Siddesha Hanumanthappa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Siddesha Hanumanthappa.

Ethics declarations

Conflict of Interest

On behalf of all authors, the corresponding author states that there is 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

Hanumanthappa, S. Damage Detection in Steel Beams Using Generalized Flexibility Quotient Difference Based Damage Index and Artificial Neural Network. J. Vib. Eng. Technol. 12, 2715–2728 (2024). https://doi.org/10.1007/s42417-023-01009-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42417-023-01009-0

Keywords

Search

Navigation