Title of article :
Determination of Damage in Reinforced Concrete Frames withShear Walls Using Self-Organizing Feature Map
Author/Authors :
Nikoo, Mehdi young Researchers and Elite Club - Ahvaz Branch - Islamic Azad University, Ahvaz, Iran , Sadowski, Aukasz faculty of Civil Engineering - Wroclaw University of Science and Technology - Wybrzeze Wyspianskiego Wroclaw, Poland , Khademi, Faezehossadat Civil and Environmental Engineering Department - Illinois Institute of Technology,Chicago, USA , Nikoo, Mohammad SAMA Technical and Vocational Training College - Islamic Azad University - Ahvaz Branch, Ahvaz, Iran
Abstract :
The paper presents the use of a self-organizing feature map (SOFM) for determining damage in reinforced concrete frames withs hear 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.
Farsi abstract :
فاقد چكيده فارسي
Keywords :
Determination of Damage , Reinforced Concrete Frames , Shear Walls , Self-Organizing Feature Map
Journal title :
Applied Computational Intelligence and Soft Computing
