Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/12276
Title: Predicting piezometric water level in dams via artificial neural networks
Authors: Rankovic, Vesna
Novaković, Aleksandar
Grujovic, Nenad
Divac, dejan
Milivojevic, Nikola
Issue Date: 2014
Abstract: The safety control of dams is based on measurements of parameters of interest such as seepage flows, seepage water clarity, piezometric levels, water levels, pressures, deformations or movements, temperature variations, loading conditions, etc. Interpretation of these large sets of available data is very important for dam health monitoring and it is based on mathematical models. Modelling seepage through geological formations located near the dam site or dam bodies is a challenging task in dam engineering. The objective of this study is to develop a feedforward neural network (FNN) model to predict the piezometric water level in dams. An improved resilient propagation algorithm has been used to train the FNN. The measured data have been compared with the results of FNN models and multiple linear regression (MLR) models that have been widely used in analysis of the structural dam behaviour. The FNN and MLR models have been developed and tested using experimental data collected during 9 years. The results of this study show that FNN models can be a powerful and important tool which can be used to assess dams. © 2013 Springer-Verlag London.
URI: https://scidar.kg.ac.rs/handle/123456789/12276
Type: article
DOI: 10.1007/s00521-012-1334-2
ISSN: 0941-0643
SCOPUS: 2-s2.0-84900672832
Appears in Collections:Faculty of Engineering, Kragujevac

Page views(s)

126

Downloads(s)

4

Files in This Item:
File Description SizeFormat 
PaperMissing.pdf
  Restricted Access
29.86 kBAdobe PDFThumbnail
View/Open


Items in SCIDAR are protected by copyright, with all rights reserved, unless otherwise indicated.