• DocumentCode
    671768
  • Title

    Dam seepage analysis based on artificial neural networks: The hysteresis phenomenon

  • Author

    Santillan, D. ; Fraile-Ardanuy, Jesus ; Toledo, M.A.

  • Author_Institution
    Dept. de Ing. Civil: Hidraulica y Energetica, Univ. Politec. de Madrid, Madrid, Spain
  • fYear
    2013
  • fDate
    4-9 Aug. 2013
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    Seepage flow measurement is an important behavior indicator when providing information about dam performance. The main objective of this study is to analyze seepage by means of an artificial neural network model. The model is trained and validated with data measured at a case study. The dam behavior towards different water level changes is reproduced by the model and a hysteresis phenomenon detected and studied. Artificial neural network models are shown to be a powerful tool for predicting and understanding seepage phenomenon.
  • Keywords
    condition monitoring; dams; flow measurement; hysteresis; neural nets; artificial neural network model; dam behavior indicator; dam monitoring; dam seepage analysis; hysteresis phenomenon; seepage flow measurement; water level; Artificial neural networks; Data models; Reservoirs; Rocks; Stress; Temperature measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2013 International Joint Conference on
  • Conference_Location
    Dallas, TX
  • ISSN
    2161-4393
  • Print_ISBN
    978-1-4673-6128-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.2013.6707110
  • Filename
    6707110