Damage identification in a ship's structure using neural networks

A. Zubaydi, M. R. Haddara*, A. S.J. Swamidas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

74 Citations (Scopus)


Visual inspection of large and complex structures such as a ship is difficult and costly due to problems of accessibility. In this paper, a neural network technique is developed for identifying the damage occurence in the side shell of a ship's structure. The side shell is modeled as a stiffened plate. The input to the network is the autocorrelation function of the vibration response of the structure. The response was obtained using a finite element model of the structure. The output is a single function Gr(zrr), which was formed by adding together the damping and a part of the restoring forces. The function is used to identify not only the damage occurence in the model but also its extent and location. The results show that the method presented in this work is successful in identifying the occurence of damage. The detection of the extent and location of damage is promising, however, more work has to be done in this area.

Original languageEnglish
Pages (from-to)1187-1200
Number of pages14
JournalOcean Engineering
Issue number10
Publication statusPublished - 5 Apr 2002
Externally publishedYes


  • Autocorrelation functions
  • Crack detection
  • Neural networks
  • Parametric identification
  • Vibration of stiffened plates


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