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
Link To Document :
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