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Influence of corrosion degradation on the dynamic response of steel plates

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Corrosion, particularly in marine and offshore environments, often leads to material loss and surface irregularities that compromise structural integrity. Traditional non-destructive testing methods, such as ultrasonic thickness measurements, are limited in regard to detecting widespread or irregular corrosion damage. This study explores the use of vibration-based analysis to assess the effects of corrosion by examining changes in the dynamic behaviour—specifically, the natural frequencies—of steel plates. Numerical simulations are conducted using Abaqus that include random surface irregularities, modelled with Gaussian random fields, to represent generalised corrosion. Experimental validation involves steel plates subjected to accelerated electrochemical corrosion, with degradation assessed based on mass loss and ultrasonic thickness measurements. Changes in modal parameters due to progressive corrosion are recorded using impact hammer excitation and accelerometers. The results show a clear relationship between corrosioninduced thickness reduction and shifts in modal characteristics. The findings demonstrate that modal analysis offers a viable, non-invasive method for detecting and evaluating global corrosion damage in large-scale steel structures.
Rocznik
Tom
Strony
132--140
Opis fizyczny
Bibliogr. 25 poz., rys.
Twórcy
autor
  • Institute of Naval Architecture, Gdańsk University of Technology, Gdańsk , Poland
autor
  • Faculty of Mechanical Engineering and Ship Technology, Gdansk University of Technology, Gdansk, Poland
  • Faculty of Mechanical Engineering and Ship Technology, Gdansk University of Technology, Gdansk, Poland
  • Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Gdansk, Poland
Bibliografia
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  • 2. Roch E, Zima B, Woloszyk K, Garbatov Y. Guided waves in ship structural health monitoring–A feasibility study. Polish Maritime Research 2023, vol. 30, pp. 76–84 https://doi.org/10.2478/pomr-2023-0023.
  • 3. Cawley P, Adams RD. The location of defects in structures from measurements of natural frequencies. J Strain Anal Eng Des 1979, vol. 14, pp. 49–57. https://doi.org/10.1243/03093247V142049.
  • 4. Abdo MAB, Hori M. A numerical study of structural damage detection using changes in the rotation of mode shapes, J Sound Vib 2002, vol. 251, pp. 227–239. https://doi.org/10.1006/jsvi.2001.3989.
  • 5. Kim JT, Ryu YS, Cho HM, Stubbs N. Damage identification in beam-type structures: Frequency-based method vs mode-shape-based method. Eng Struct 2003, vol. 25, pp. 57–67. https://doi.org/10.1016/S0141-0296(02)00118-9.
  • 6. Dessi D, Camerlengo G. Damage identification techniques via modal curvature analysis: Overview and comparison. Mech Syst Signal Process 2015, vol. 52–53, pp. 181–205. https://doi.org/10.1016/j.ymssp.2014.05.031.
  • 7. Pandey AK, Biswas M, Samman MM. Damage detection from changes in curvature mode shapes. J Sound Vib 1991, vol. 145, pp. 321–332. https://doi.org/10.1016/0022-460X(91)90595-B.
  • 8. Raghavendrachar M, Aktan AE. Flexibility by multireference impact testing for bridge diagnostics. Journal of Structural Engineering 1992, vol. 118, pp. 2186–2203. https://doi.org/10.1061/(asce)0733-9445(1992)118:8(2186).
  • 9. Avci O, Abdeljaber O, Kiranyaz S, Hussein M, Gabbouj M, Inman DJ. A review of vibration-based damage detection in civil structures: From traditional methods to machine learning and deep learning applications. Mech Syst Signal Process 2021, vol. 147, pp. 1–45. https://doi.org/10.1016/j.ymssp.2020.107077.
  • 10. Cao S, Lu Z, Wang D, Xu C. Robust multi-damage localization in plate-type structures via adaptive denoising and data fusion based on full-field vibration measurements. Measurement (Lond) 2021, vol. 178, pp. 1-13. https://doi.org/10.1016/j.measurement.2021.109393.
  • 11. Trendafilova I, Manoach E. Vibration-based damage detection in plates by using time series analysis. Mech Syst Signal Process 2008, vol. 22, pp. 1092–1106. https://doi.org/10.1016/j.ymssp.2007.11.020.
  • 12. Dziedziech K, Staszewski WJ, Basu B, Uhl T. Wavelet-based detection of abrupt changes in natural frequencies of timevariant systems. Mech Syst Signal Process 2015, vol. 64–65, pp. 347–359. https://doi.org/10.1016/j.ymssp.2015.03.012.
  • 13. Shahsavari V, Chouinard L, Bastien J. Wavelet-based analysis of mode shapes for statistical detection and localization of damage in beams using likelihood ratio test. Eng Struct 2017, vol. 132, pp. 494–507. https://doi.org/10.1016/j.engstruct.2016.11.056.
  • 14. Katunin A. Identification of structural damage using S-transform from 1D and 2D mode shapes. Measurement (Lond) 2021, vol. 173, pp. 1–18. https://doi.org/10.1016/j.measurement.2020.108656.
  • 15. Zhong S, Oyadiji SO. Crack detection in simply supported beams without baseline modal parameters by stationary wavelet transform. Mech Syst Signal Process 2007, vol. 21, pp. 1853–1884. https://doi.org/10.1016/j.ymssp.2006.07.007.
  • 16. Zhong S, Oyadiji SO. Detection of cracks in simplysupported beams by continuous wavelet transform of reconstructed modal data. Comput Struct 2011, vol. 89, pp. 127–148. https://doi.org/10.1016/j.compstruc.2010.08.008.
  • 17. Yanez-Borjas JJ, Valtierra-Rodriguez M, Camarena-Martinez D, Amezquita-Sanchez JP. Statistical time features for global corrosion assessment in a truss bridge from vibration signals. Measurement (Lond) 2020, vol. 160, pp. 1–13. https://doi.org/10.1016/j.measurement.2020.107858.
  • 18. Zheng Z, Dai H. Simulation of multi-dimensional random fields by Karhunen–Loeve expansion. Comput Methods Appl Mech Eng 2017, vol. 324, pp. 221–247. https://doi.org/10.1016/j.cma.2017.05.022.
  • 19. Kramer PR, Kurbanmuradov O, Sabelfeld K. Comparative analysis of multiscale Gaussian random field simulation algorithms. J Comput Phys 2007, vol. 226, pp. 897–924. https://doi.org/10.1016/j.jcp.2007.05.002.
  • 20. Constantine P. Random field simulation. August 27, 2024. Retrieved from https://www.mathworks.com/ matlabcentral/fileexchange/27613-random-fieldsimulation, MATLAB Central File Exchange.
  • 21. Kiciński R, Kubit A. Small caliber bulletproof test of warships’ hulls. Materials 2020, vol. 13, pp. 1–15. https://doi.org/10.3390/ma13173848.
  • 22. Wang M, Wang D, Zheng G. Joint dynamic properties identification with partially measured frequency response function. Mech Syst Signal Process 2012, vol. 27, pp. 499–512. https://doi.org/10.1016/j.ymssp.2011.09.024.
  • 23. Niu Z. Two-step structural damage detection method for shear frame structures using FRF and Neumann series expansion. Mech Syst Signal Process 2021, vol. 149, pp. 1–18. https://doi.org/10.1016/j.ymssp.2020.107185.
  • 24. Fathi A, Esfandiari A, Fadavie M, Mojtahedi A. Damage detection in an offshore platform using incomplete noisy FRF data by a novel Bayesian model updating method. Ocean Engineering 2020, vol. 217, pp. 1–15. https://doi.org/10.1016/j.oceaneng.2020.108023.
  • 25. Porcu MC, Patteri DM, Melis S, Aymerich F. Effectiveness of the FRF curvature technique for structural health monitoring. Constr Build Mater 2019, vol. 226, pp. 173–187. https://doi.org/10.1016/j.conbuildmat.2019.07.123.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-86f09550-dde6-467b-891a-8246e7731258
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