• DocumentCode
    2707064
  • Title

    Prediction of the Acoustic Pressure Backscattered by a Steel Tube Using Neural Networks Approach

  • Author

    Dariouchy, A. ; Aassif, E. ; Maze, G. ; Latif, R. ; Decultot, D. ; Laaboubi, M.

  • Author_Institution
    Univ. Ibn Zohr, Agadir
  • fYear
    2007
  • fDate
    28-30 March 2007
  • Firstpage
    117
  • Lastpage
    119
  • Abstract
    A new approach is used to predict the pressure backscattered by a tube using the artificial neural networks (ANNs) techniques. The studied tube consists of steel. During the development of the network, several configurations are evaluated for various radius ratio b/a (a: outer radius, b: inner radius of tube). The multilayer perceptron (MLP) is used in the current study. The optimal model selected is a network with one hidden layer. This model is able to predict the pressure backscattered with a mean relative error (MRE) of about a 1.6%. The comparison of the obtained and the experimental results indicate that the ANN method is suitable to be used to predict this one.
  • Keywords
    acoustic waves; backscatter; multilayer perceptrons; pipes; production engineering computing; steel; acoustic pressure backscattered; artificial neural network; mean relative error; multilayer perceptron; steel tube; Artificial neural networks; Geometry; Multilayer perceptrons; Neural networks; Predictive models; Resonance; Statistical analysis; Steel; Testing; Uncertainty; Multilayer perceptron; Neural network; Pressure backscattered; Steel tube;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Intelligent Informatics, 2007. ISCIII '07. International Symposium on
  • Conference_Location
    Agadir
  • Print_ISBN
    1-4244-1158-0
  • Electronic_ISBN
    1-4244-1158-0
  • Type

    conf

  • DOI
    10.1109/ISCIII.2007.367373
  • Filename
    4218406