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International Journal of Steel Structures

, Volume 19, Issue 2, pp 413–421 | Cite as

Model Updating and Crack Detection for Elastically Restrained Tapered Cantilever-Type Beam Using Natural Frequencies

  • Jong-Won LeeEmail author
Article
  • 37 Downloads

Abstract

The current paper presents a method to update the baseline model and detect cracks in tapered cantilever pipe-type beams which are restrained by a translational and rotational spring with a tip mass at the free end using natural frequencies. Modal parameters of intact beam are obtained by applying boundary conditions to a general solution for tapered beam. The equivalent bending stiffness is used to calculate natural frequencies of cracked beams. An experimental study is carried out. The translational and rotational spring constants are updated to establish a baseline model using a neural network. Then, several numerical analyses of the cracked beams are carried out to extract the natural frequencies, and those are used in constructing the training patterns of a neural network. The committee of neural networks are employed to identify the cracks. The crack identifications are carried out for the 3 damage cases, and it is found that the estimated crack locations and sizes agree reasonably well with the exact values.

Keywords

Crack identification Elastically restrained Natural frequency Tapered cantilever-type beam Neural network 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No. 2017R1A2B4006722).

References

  1. Dilena, M., Dell’Oste, M. F., & Morassi, A. (2011). Detecting cracks in pipes filled with fluid from changes in natural frequencies. Mechanical Systems and Signal Processing, 25, 3186–3197.CrossRefGoogle Scholar
  2. Gorman, D. J. (1975). Free vibration analysis of beams and shafts. New York: Wiley.Google Scholar
  3. Lee, J. W. (2016). Crack identification method for tapered cantilever pipe-type beam using natural frequencies. International Journal of Steel Structures, 16, 467–476.CrossRefGoogle Scholar
  4. Lee, J. W., Kim, S. R., & Huh, Y. C. (2014). Pipe crack identification based on the energy method and committee of neural networks. International Journal of Steel Structures, 14, 345–354.CrossRefGoogle Scholar
  5. Matsuoka, K. (1992). Noise injection into inputs in back-propagation. IEEE Transactions on Systems, Man and Cybernetics, 22, 436–440.CrossRefGoogle Scholar
  6. Naniwadekar, M. R., Naik, S. S., & Maiti, S. K. (2008). On prediction of crack in different orientations in pipe using frequency based approach. Mechanical Systems Signal Processing, 22, 693–708.CrossRefGoogle Scholar
  7. Sinou, J. J. (2012). On the use of non-linear vibrations and the anti-resonances of higher-order frequency response functions for crack detection in pipeline beam. Mechanical Research Communications, 43, 87–95.CrossRefGoogle Scholar
  8. Wang, Y. M., Chen, X. F., & He, Z. J. (2011). Daubechies wavelet finite element method and genetic algorithm for detection of pipe crack. Nondestructive Test Evaluation, 26, 87–99.CrossRefGoogle Scholar
  9. Ye, J., He, Y., Chen, X., Zhai, Z., Wang, Y., & He, Z. (2010). Pipe crack identification based on finite element method of second generation wavelets. Mechanical Systems and Signal Processing, 24, 379–393.CrossRefGoogle Scholar

Copyright information

© Korean Society of Steel Construction 2018

Authors and Affiliations

  1. 1.Department of Architectural EngineeringNamseoul UniversityCheonan-siKorea

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