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Applied Computational Intelligence and Soft Computing
Volume 2017 (2017), Article ID 3508189, 10 pages
Research Article

Determination of Damage in Reinforced Concrete Frames with Shear Walls Using Self-Organizing Feature Map

1Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
2Faculty of Civil Engineering, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
3Civil and Environmental Engineering Department, Illinois Institute of Technology, Chicago, IL 60616, USA
4SAMA Technical and Vocational Training College, Islamic Azad University, Ahvaz Branch, Ahvaz, Iran

Correspondence should be addressed to Ɓukasz Sadowski

Received 6 January 2017; Accepted 13 April 2017; Published 15 May 2017

Academic Editor: Erich Peter Klement

Copyright © 2017 Mehdi Nikoo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The paper presents the use of a self-organizing feature map (SOFM) for determining damage in reinforced concrete frames with shear walls. For this purpose, a concrete frame with a shear wall was subjected to nonlinear dynamic analysis. The SOFM was optimized using the genetic algorithm (GA) in order to determine the number of layers, number of nodes in the hidden layer, transfer function type, and learning algorithm. The obtained model was compared with linear regression (LR) and nonlinear regression (NonLR) models and also the radial basis function (RBF) of a neural network. It was concluded that the SOFM, when optimized with the GA, has more strength, flexibility, and accuracy.