Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
Dam Seepage Analysis Based on Artificial Neural Networks: the Hysteresis Phenomenon
Year:2013
Research Areas
  • Electric engineers, electronic and automatic (eil),
  • Civil engineering and architecture
Information
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.
International
Si
Congress
The 2013 International Joint Conference onNeural Networks (IJCNN),
960
Place
Dallas (USA)
Reviewers
Si
ISBN/ISSN
978-1-4673-6128-6
10.1109/IJCNN.2013.6707110
Start Date
04/08/2013
End Date
09/08/2013
From page
1
To page
8
Dam seepage analysis based on artificial neural networks: the hysteresis phenomenon
Participants
  • Autor: Jose Jesus Fraile Ardanuy (UPM)
  • Autor: David Santillan Sanchez (UPM)
  • Autor: Miguel Angel Toledo Municio (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Hidroinformática y Gestión del Agua
  • Grupo de Investigación: Grupo de Sistemas Dinámicos, Aprendizaje y Control (SISDAC)
  • Departamento: Ingeniería Civil: Hidráulica y Energética
  • Departamento: Tecnologías Especiales Aplicadas a la Telecomunicación
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