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CivilComp Proceedings
ISSN 17593433 CCP: 78
PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TO CIVIL AND STRUCTURAL ENGINEERING Edited by: B.H.V. Topping
Paper 57
A Neural Network Approach for Seepage Control in Earth Dams J. Veiga Carvalho, J.L.C. Gutiérrez and C. Romanel
Department of Civil Engineering, Catholic University of Rio de Janeiro, Brazil , "A Neural Network Approach for Seepage Control in Earth Dams", in B.H.V. Topping, (Editor), "Proceedings of the Seventh International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering", CivilComp Press, Stirlingshire, UK, Paper 57, 2003. doi:10.4203/ccp.78.57
Keywords: water discharge, dam monitoring, time series, drainage, Funil dam, CorumbaI dam.
Summary
Dams are monitored in order to verify whether their performance is consistent
with the original design expectations. Performance monitoring of dams is generally
accomplished by reviewing and analysing data collected from instruments which
measure critical indicators of engineering behaviour. Instrumented monitoring
includes measurements of displacement, strain, stress, pressure, loads on structural
members, seepage and drainage along with environmental factors that affect the dam
behaviour such as temperature, reservoir level and precipitation.
In this work, artificial neural networks (ANN) and the Box Jenkins approach [1] are used to model time series consisted of a sequence of water discharges, measured along several years of continuous observation, through the foundation of two large Brazilian dams (Funil and CorumbaI dams). The sequence of water discharges through the soil foundation were modelled using multilayer feedforward networks. The algorithm for ANN training was either the LM (LevenbergMarquardt) method, for the Funil dam time series, or the backpropagation algorithm, with the descendent gradient method, for the data sets collected at the CorumbaI dam. Several neural network configurations, with different numbers of input parameters and hidden neurons, were trained and tested to assess their influence on the model behaviour. The time series for CorumbaI dam consisted of 130 patterns, measured at time intervals of 15 days, beginning in March 1997 and extending to December 2002. Two data sets were considered in this analysis: the first one composed of 104 samples, used during the training phase, and the remaining 26 patterns, which form the second data set, reserved for the validation process. Figures 57.1 and 57.2 compare the actual measured values with respective neural network forecasts for the case of CorumbaI dam. The computed results are quite helpful to control seepage under the dam foundation, since a study with conventional methods would require a 3D finite element model and a comprehensive field investigation in order to determine the local soil properties which, in this particular area, presents a system of fractures that rends the analysis a quite difficult task. The ANN performances were considered better than those obtained by the AR [1] model. However, the BoxJenkins approach was a helpful tool for preliminary analyses, which permitted an a priori estimate of the required number of sequence elements for the ANN models.
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