Shearography-Wavelet-Based Damage Detection Methodology for Aluminum Beams

Document Type : Regular Paper

Authors

1 M.Sc. Student, Civil Engineering Department, University of Birjand, Birjand, Iran

2 Associate Professor, Department of Civil Engineering, University of Birjand, Birjand, Iran

3 Assistant Professor, Department of Civil Engineering, University of Birjand, Birjand, Iran

4 Professor of Mechanical Engineering, University of Lisbon, Instituto Superior Tecnico, Lisbon, Portugal

5 Assistant Professor, Department of Mechanical Engineering, DEM-ISEP, Instituto Politécnico do Porto, Porto, Portugal

Abstract

In this paper, aluminum beams in undamaged status and with single and double damage scenarios as slots with the ratio of the slot depth to the beam thickness of 7% and 28% were constructed at the University of Lisbon, Portugal. Then, with the help of the shrearography method, the modal rotations of each beam were calculated for the first to third vibration mode shapes. By deriving the modal rotations, the modal strains were obtained and introduced as the input of 22 different families of 2D wavelet transforms with three different scales 1, 7, and 15. Utilizing the wavelet coefficients as damage indices, the results showed that the sensitivity of modal curvatures is higher than other modal data for identifying the location of damages. In addition, among scales 1, 7, and 15, considering scale 7 for wavelet families provides more suitable results. On the other hand, the sinc and isodog wavelet families showed a better ability to reveal the damage location than other wavelets. Investigating the ratio of the maximum value of the wavelet coefficients in the middle part of the beams to the maximum value of the wavelet coefficients in the boundaries showed that among the two selected wavelets, sinc and isodog, the sinc wavelet is more sensitive than the isodog wavelet in identifying damages with obtained results of 0.81, 7.81 and 27.10 for first, second and third damage scenarios, respectively. And therefore, it can be considered the best wavelet for detecting artificial damage in the tested aluminum beams.

Keywords

Main Subjects

References

[1]     Cabboi A, Gentile C, Saisi A. From continuous vibration monitoring to FEM-based damage assessment: Application on a stone-masonry tower. Construction and Building Materials 2017;156:252–65. https://doi.org/10.1016/J.CONBUILDMAT.2017.08.160.
[2]     García-Macías E, Kita A, Ubertini F. Synergistic application of operational modal analysis and ambient noise deconvolution interferometry for structural and damage identification in historic masonry structures: three case studies of Italian architectural heritage. Structural Health Monitoring 2020;19:1250–72. https://doi.org/10.1177/1475921719881450.
[3]     Fallah N, Hoseini Vaez SR, Esfandiari A. Damage identification of structures based on sensitivity equations using PCA and PSD data. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 2022:095440622211388. https://doi.org/10.1177/09544062221138846.
[4]     Jahangir H, Khatibinia M, Mokhtari Masinaei M. Damage Detection in Prestressed Concrete Slabs Using Wavelet Analysis of Vibration Responses in the Time Domain. Journal of Rehabilitation in Civil Engineering 2022;10:37–63. https://doi.org/10.22075/jrce.2021.23385.1510.
[5]     Hanteh M, Rezaifar O. Damage detection in precast full panel building by continuous wavelet analysis analytical method. Structures 2021;29:701–13. https://doi.org/10.1016/j.istruc.2020.12.002.
[6]     Hanteh M, Rezaifar O, Gholhaki M. Selecting the appropriate wavelet function in the damage detection of precast full panel building based on experimental results and wavelet analysis. Journal of Civil Structural Health Monitoring 2021;11:1013–36. https://doi.org/10.1007/s13349-021-00497-6.
[7]     Entezami A, Sarmadi H, Behkamal B, Mariani S. Health Monitoring of Large-Scale Civil Structures: An Approach Based on Data Partitioning and Classical Multidimensional Scaling. Sensors 2021;21:1646. https://doi.org/10.3390/s21051646.
[8]     Livitsanos G, Shetty N, Verstrynge E, Wevers M, Hemelrijck D Van, Aggelis DG. Acoustic Emission Health Monitoring of Historical Masonry to Evaluate Structural Integrity under Incremental Cyclic Loading. Proceedings 2018;2. https://doi.org/10.3390/ICEM18-05417.
[9]     Ubertini F, D’Alessandro A, Downey A, García-Macías E, Laflamme S, Castro-Triguero R. Recent Advances on SHM of Reinforced Concrete and Masonry Structures Enabled by Self-Sensing Structural Materials. Proceedings 2018;2. https://doi.org/10.3390/ecsa-4-04889.
[10]   Katunin A, Araújo Dos Santos J V., Lopes H. Application of wavelet analysis to differences in modal rotations for damage identification. IOP Conference Series: Materials Science and Engineering 2019;561. https://doi.org/10.1088/1757-899X/561/1/012024.
[11]   Lopes H, Ribeiro J, Dos Santos AJV. Interferometric techniques in structural damage identification. Shock and Vibration 2012;19:835–44. https://doi.org/10.3233/SAV-2012-0692.
[12]   Francis D, Tatam RP, Groves RM. Shearography technology and applications: a review. Measurement Science \& Technology 2010;21:102001. https://doi.org/10.1088/0957-0233/21/10/102001.
[13]   Zhao Q, Dan X, Sun F, Wang Y, Wu S, Yang L. Digital shearography for NDT: Phase measurement technique and recent developments. Applied Sciences (Switzerland) 2018;8. https://doi.org/10.3390/app8122662.
[14]   Akbari D, Soltani N, Farahani M. Numerical and experimental investigation of defect detection in polymer materials by means of digital shearography with thermal loading. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2013;227:430–42. https://doi.org/10.1177/0954405412473054.
[15]   Katunin A, Przystałka P. Damage assessment in composite plates using fractional wavelet transform of modal shapes with optimized selection of spatial wavelets. Engineering Applications of Artificial Intelligence 2014;30:73–85. https://doi.org/10.1016/j.engappai.2014.01.003.
[16]   Zhou J, Li Z, Chen J. Damage identification method based on continuous wavelet transform and mode shapes for composite laminates with cutouts. Composite Structures 2018;191:12–23. https://doi.org/10.1016/j.compstruct.2018.02.028.
[17]   Pnevmatikos N, Konstandakopoulou F, Blachowski B, Papavasileiou G, Broukos P. Multifractal analysis and wavelet leaders for structural damage detection of structures subjected to earthquake excitation. Soil Dynamics and Earthquake Engineering 2020;139:106328. https://doi.org/10.1016/j.soildyn.2020.106328.
[18]   Jahangir H, Hasani H, Esfahani MR. Wavelet-based damage localization and severity estimation of experimental RC beams subjected to gradual static bending tests. Structures 2021;34:3055–69. https://doi.org/10.1016/j.istruc.2021.09.059.
[19]   Khanahmadi M, Gholhaki M, Rezaifar O, Dejkam B. Damage identification in steel beam structures based on the comparison of analytical results of wavelet analysis. 2023 n.d.;8. https://doi.org/10.22091/CER.2022.8340.1407.
[20]   Fakharian P, Naderpour H. Damage Severity Quantification Using Wavelet Packet Transform and Peak Picking Method. Practice Periodical on Structural Design and Construction 2022;27. https://doi.org/10.1061/(ASCE)SC.1943-5576.0000639.
[21]   Katunin A, Araújo dos Santos JV, Lopes H. Damage identification by wavelet analysis of modal rotation differences. Structures 2021;30:1–10. https://doi.org/10.1016/j.istruc.2021.01.010.
[22]   Katunin A, Lopes H, dos Santos JVA. Identification of multiple damage using modal rotation obtained with shearography and undecimated wavelet transform. Mechanical Systems and Signal Processing 2019;116:725–40. https://doi.org/10.1016/j.ymssp.2018.07.024.
[23]   Araujo dos Santos J V., Katunin A, Lopes H. Vibration-Based Damage Identification Using Wavelet Transform and a Numerical Model of Shearography. International Journal of Structural Stability and Dynamics 2019;19:1950038. https://doi.org/10.1142/S021945541950038X.
[24]   Smith M. ABAQUS/Standard User’s Manual, Version 6.9. Simulia; 2009.
[25]   Cinque D. Machine learning techniques for damage identification in beams. Università degli Studi Roma Tre, 2019.

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History

  • Receive Date: 27 December 2022
  • Revise Date: 19 January 2023
  • Accept Date: 14 March 2023

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