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Damage localization in truss girders by an application of the discrete wavelet transform

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Języki publikacji
EN
Abstrakty
EN
The paper demonstrates the potential of wavelet transform in a discrete form for structural damage localization. The efficiency of the method is tested through a series of numerical examples, where the real flat truss girder is simulated by a parameterized finite element model. The welded joints are introduced into the girder and classic code loads are applied. The static vertical deflections and rotation angles of steel truss structure are taken into consideration, structural response signals are computed at discrete points uniformly distributed along the upper or lower chord. Signal decomposition is performed according to the Mallat pyramid algorithm. The performed analyses proved that the application of DWT to decompose structural response signals is very effective in determining the location of the defect. Evident disturbances of the transformed signals, including high peaks, are expected as an indicator of the defect existence in the structure. The authors succeeded for the first time in the detection of breaking the weld in the truss node as well as proved that the defect can be located in the diagonals.
Rocznik
Strony
art. no. e144581
Opis fizyczny
Bibliogr. 26 poz., rys., tab.
Twórcy
  • Poznan University of Technology, Institute of Structural Analysis, Poland
autor
  • Poznan University of Technology, Institute of Structural Analysis, Poland
  • Poznan University of Technology, Institute of Structural Analysis, Poland
Bibliografia
  • [1] T. Kersting, et al. “High end inspection by filmless radiography on LSAW large diameter pipes”, NDT E Int., vol. 43, no. 3, pp. 206–209, 2010.
  • [2] P. Kołakowski, J. Szelążek, and K. Sekuła, “Structural health monitoring of a railway truss bridge using vibration-based and ultrasonic methods”, Smart Mater. Struct., vol. 20, no. 3, pp. 035016-1–035016-10, 2011, doi: 10.1088/0964-1726/20/3/035016.
  • [3] R. Drelich, M. Rosiak, and M. Pakula, “Application of non-contact ultrasonic method in air to study fiber-cement corrugated boards”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 2, p. e136740, 2021, doi: 10.24425/bpasts.2021.136740.
  • [4] R. Skłodowski, et al., “Identifying subsurface detachment defects by acoustic tracing”, NDT E Int., vol. 56, pp. 56–64, 2013.
  • [5] X. Wang and J. Tang, “Structural damage detection using a magnetic impedance approach with circuitry integration”, Smart Mater. Struct., vol. 20, no. 3, p. 035022, 2008, doi: 10.1088/0964-1726/20/3/035022.
  • [6] T. Chen, et al. “Feature extraction and selection for defect classification of pulsed eddy current NDT”, NDT E Int., vol. 41, no. 6, pp. 467–476, 2008.
  • [7] K. Ziopaja, Z. Pozorski, and A. Garstecki, “Damage detection using thermal experiments and wavelet transformation”, Inverse Probl. Sci. Eng., vol. 19, no. 1, pp. 127–153, 2011.
  • [8] H.T. Banks and A.K. Criner, “Thermal based methods for damage detection and characterization in porous materials”, Inverse Probl., vol. 28, no. 6, pp. 065021-1–065021-18, 2012, doi: 10.1088/0266-5611/28/6/065021.
  • [9] B. Wójcik and M. Żarski, “The measurements of surface defect area with an RGB-D camera for a BIM-backed bridge inspection”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 3, p. e137123, 2021, doi: 10.24425/bpasts.2021.137123.
  • [10] D. Cekus, P. Kwiatoń, M. Šofer, and P. Šofer, “Application of heuristic methods to identification of the parameters of discrete-continuous models”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e140150, 2022, doi: 10.24425/bpasts.2022.140150.
  • [11] T. Burczyński, W. Kuś, A. Długosz, and P. Orantek, “Optimization and defect identification using distributed evolutionary algorithms”, Eng. Appl. Artif. Intell., vol. 17, pp. 337–344, 2004.
  • [12] M. Skowron, “Application of deep learning neural networks for the diagnosis of electrical damage to the induction motor using the axial flux”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 5, pp. 1031–1038, 2020, doi: 10.24425/bpasts.2020.134664.
  • [13] Z. Waszczyszyn and L. Ziemiański, “Neural networks in mechanics of structures and materials – new results and prospects of applications”, Comput. Struct., vol. 79, no. 22–25, pp. 2261–2276, 2001.
  • [14] A. Knitter-Piątkowska, M. Guminiak, and M. Przychodzki, “Application of discrete wavelet transformation to defect detection in truss structures with rigidly connected bars”, Eng. Trans., vol. 64, no. 2, pp. 157–170, 2016.
  • [15] M. Guminiak and A. Knitter-Piątkowska, “Selected problems of damage detections in internally supported plates using one-dimensional Discrete Wavelet Transform”, J. Theor. Appl. Mech., vol. 56, no. 3, pp. 631–644, 2018.
  • [16] A. Knitter-Piątkowska and A. Dobrzycki, “Application of wavelet transform to damage identification in the steel structure elements”, Appl. Sci., vol. 10, no. 22, pp. 8198-1–8198-12, 2020, doi: 10.3390/app10228198.
  • [17] I. Daubechies, Ten lectures on wavelets. Philadelphia: Society for Industrial and Applied Mathematics, 1992.
  • [18] M. Hanteh, O. Rezaifar, and M. Gholhaki, “Selecting the appropriate wavelet function in the damage detection of precast full panel building based on experimental results and wavelet analysis”, J. Civ. Struct. Health Monit., vol. 11, pp. 1013–1036, 2021, doi: 10.1007/s13349-021-00497-6.
  • [19] M. Kamiński, “Interface defects in unidirectional composites by multiresolutional finite element analysis”, Comput. Struct., vol. 84, no. 19–20, pp. 1190–1199, 2006.
  • [20] A. Knitter-Piatkowska and T. Garbowski, “Damage detection through wavelet transform and inverse analysis,” in Proc. VI International Conference on Adaptive Modeling and Simulation ADMOS, 2013, pp. 389–400.
  • [21] A. Knitter-Piątkowska and T. Garbowski, “Wavelet transform and soft computing in damage identification”, in Proc. International Conference on Engineering and Applied Sciences Optimization OPT-i, 2014, pp. 21752188.
  • [22] M. Rucka and K. Wilde, “Neuro-wavelet damage detection technique in beam, plate and shell structures with experimental validation”, J. Theor. Appl. Mech., vol. 48, no. 3, pp. 579–604, 2010.
  • [23] S.G. Mallat, A wavelet tour of signal processing. San Diego, Academic Press, 1999.
  • [24] B. Svendsen, G.T. Fröseth, and A. Rönnquist, “Damage detection applied to a full-scale steel bridge using temporal moments”, Shock Vibr., pp. 3083752-1–3083752-16, 2020, doi: 10.1155/2020/3083752.
  • [25] R. Ferrari, G. Cocchetti, and E. Rizzi, “Reference structural investigation on a 19th-century arch iron bridge loyal to design-stage conditions”, Int. J. Archit. Herit., vol. 14, no. 10, pp. 14251455, 2020, doi: 10.1080/15583058.2019.1613453.
  • [26] K.M. Kensek, Building Information Modeling. London: Routledge, 2014.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a4b6052a-58b0-4f0b-972f-61848b8062bf
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