Author(s):
1. Vesna Ranković, Faculty of Engineering, University of Kragujevac, Sestre Janjić 6, 34000 Kragujevac, Srbija 2IMW Ins, Serbia

Abstract:
The safety control of dam is supported by monitoring activities and is based on models. The variations of hydrostatic pressure and temperature are the main variables that should be taken into account when analysing the results of the dam observations. Deterministic models based on mechanical principles are often difficult to construct. Statistical procedures, such as multiple linear regression (MLR), have been applied to dam safety to determine the influence of external loads on the structure deformation. The relations between these loads and dam behavior are nonlinear. Radial basis function (RBF) neural network can be successfully applied to function approximation, forecast, and dynamic systems identification. Neural network modeling from measured data is effective tool for the approximation of uncertain nonlinear systems. This paper presents novel approach based on the use of RBF network to estimate dam behavior. Mathematical models based on experimental data are developed. MLR and RBF neural network models for prediction of dam behavior have been compared with the measured data on the basis of correlation coefficient.

Thematic field:
MECHANICS AND DESIGN

Opening paper:
Yes

Date of abstract submission:
12.03.2011.

Number of visits:
819

Conference:
11^th International Conference on Accomplishments in Mechanical and Industrial Engineering

Applied paper from author