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PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING
Edited by: Y. Tsompanakis, B.H.V. Topping
Application of Soft Computing Techniques to Dam Safety Monitoring
V. Rankovic1, N. Grujovic1, D. Divac2, N. Milivojevic2 and G. Milanovic3
1Department for Applied Mechanics and Automatic Control, Faculty of Mechanical Engineering, University of Kragujevac, Serbia
V. Rankovic, N. Grujovic, D. Divac, N. Milivojevic, G. Milanovic, "Application of Soft Computing Techniques to Dam Safety Monitoring", in Y. Tsompanakis, B.H.V. Topping, (Editors), "Proceedings of the Second International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 13, 2011. doi:10.4203/ccp.97.13
Keywords: dam safety, feedforward neural network, adaptive neuro-fuzzy inference system, dam behavior, radial displacement, modeling.
The safety control of a dam is supported by monitoring activities and is based on models. Deterministic and statistical methods have been used to develop models to predict dam behaviour. A hybrid method has also been applied to forecast behaviour. The deterministic modelling requires solving differential equations for which closed form solutions may be difficult or impossible to obtain. The advantages of the statistical method, such as multiple linear regression, consists in the simplicity of formulation and the speed of execution.
The radial displacements of points of the dam are important behaviour indicators and they are nonlinear function of hydrostatic pressure, temperature and other unexpected unknown causes. In dam engineering, multilayer perceptron neural network models , models based on wavelet networks [2,3] and neuro-fuzzy models  have been developed for the prediction of dam displacements.
The major objective of the study presented in this paper is to construct a high-quality feedforward neural network (FNN) and adaptive neuro-fuzzy inference systems (ANFIS) to predict the radial displacement of arch dams and to demonstrate their application to identifying complex nonlinear relationships between input and output variables. The FNN and ANFIS models were developed and tested using experimental data which are collected over eleven years.
The two-layer network with a log-sigmoid transfer function at the hidden layer and a linear transfer function at the output layer were used. The optimal number of hidden neurons is sixteen. Fuzzy partitioning of the input variables of the ANFIS was realized by selection of the two primary fuzzy sets. The Gaussian membership function was adopted. The ANFIS models in the considered example have thirty-two rules. The FNN model has a slightly higher coefficient of correlation values for the training set, but a slightly lower coefficient of correlation values for the test set. Comparing the modelled values using FNN and ANFIS with the experimental data indicates that soft computing models provide accurate results. These models can be applied to the prediction of displacements.
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