عنوان مقاله [English]
The objective of this study is to investigate the adequacy of Artificial Neural Networks (ANN) as a method to determine the dynamic response of fluid in elevated water tanks. For this purpose, an ANN models were proposed to estimate the hydrodynamic pressure in bottom of container and sloshing of water surface. ANN models were developed, trained and tested in a based MATLAB program. Nonlinear dynamic analysis using Finite Element Application (FEA) based ANSYS was used to generate training and testing set of ANN models. In the ANN models, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm was employed using a scaled conjugate gradient. The data used in the ANN model are arranged in a format of three input parameters that cover the time history of earthquake horizontal acceleration, container ceiling displacement and base shear force.The performance of the new ANN model is compared with ANSYS results. The comparison indicates that the ANN model has strong potential to estimate hydrodynamic pressure. It was demonstrated that the neural network based approach is highly successful to estimate response of fluid subjected to earthquake without using complex fluid elements.
 Caglar, N., Elmas, M., Dere Yaman, Z., & Saribiyik, M. (2008). Neural networ ks in 3-dimensional dynamic analysis of reinforced concrete buildings. Construction and Building Materials, 22, 788-800.
 Kulahci, F., Inceoz, M., Dogru, M., Aksoy, E., & Baykara, O. (2009). Artiﬁcial neural network model for earthquake prediction with radon monitoring. Applied Radiation and Isotopes, 67, 212-219.
 Kim, Y. S., & Kim, B. T. (2008). Prediction of relative crest settlement of concrete-faced rockﬁll dams analyzed using an artiﬁcial neural network model. Computers and Geotechnics , 35, 313–322.
 Mansour, M., Dicleli, M., Lee, J., & Zhang, J. (2004). Predicting the shear strength of reinforced concrete beams using artiﬁcial neural networks. Engineering Structures, 26, 781–799.
 Choubey, A., Segal, D., & Tandon, N. (2006). Finite element analysis of vessels to study changes in natural frequencies due to cracks. International Journal of Pressure Vessels and Piping, 83, 181–187.
 Memari, A. M., Ahmadi, M. M., & Rezaee, B. (1992). “Behavior of reinforced concrete water towers during Manjil-Roudbar earthquake of June 1990”. 10th World Conference on Earthquake Engineering, (pp. Vol. 9, , pages 4953). Balkema, Rotterdam.
 Liu, He & Schubert, Daniel H. (2010) “Effects of Nonlinear Geometric and Material Properties on the Seismic Response of Fluid/Tank Systems”. ANSYS international conference.
 Kalani Sarokolayi, L., Navayineya, B., Hosainalibegi, M., & Vaseghi Amiri, J. (2008). “Dynamic Analysis of Water Tanks with Interaction between Fluid and Structure”. Beijing, China: The 14th World Conference on Earthquake Engineering.
 Adeli, H. (2001), “Neural network in civil engineering”, Comp-Aided Civil and Infrastructures Eng, J, 6, pp 126-142.
 Fausett, L.V. (1994), “Fundamentals neural networks: Architecture, algorithms, and applications”, Prentice-Hall, Englewood Cliffs, N.J.