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


Autoria(s): Santillan Sanchez, David; Fraile Ardanuy, José Jesús; Toledo Municio, Miguel Angel
Data(s)

2013

Resumo

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.

Formato

application/pdf

Identificador

http://oa.upm.es/30159/

Idioma(s)

eng

Publicador

E.T.S.I. Telecomunicación (UPM)

Relação

http://oa.upm.es/30159/1/INVE_MEM_2013_158308.pdf

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6707110&tag=1

info:eu-repo/semantics/altIdentifier/doi/10.1109/IJCNN.2013.6707110

Direitos

http://creativecommons.org/licenses/by-nc-nd/3.0/es/

info:eu-repo/semantics/openAccess

Fonte

2013 International Joint Conference on Neural Networks (IJCNN) | 2013 International Joint Conference on Neural Networks (IJCNN) | 04/08/2013 - 09/08/2013 | Dallas, Texas, EE.UU

Palavras-Chave #Ingeniería Civil y de la Construcción
Tipo

info:eu-repo/semantics/conferenceObject

Ponencia en Congreso o Jornada

PeerReviewed