Determination of Damage in Reinforced Concrete Frames with Shear Walls Using Self-Organizing Feature Map. (15th May 2017)

• Record Type: Journal Article
• Authors: Nikoo, Mehdi; Sadowski, Łukasz; Khademi, Faezehossadat; Nikoo, Mohammad
• Other Names: Klement Erich Peter Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2017(2017)
• Publication Date: 2017-05-15
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2017/3508189
• Languages: English
• ISSNs: 1687-9724

Abstract

Abstract : 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.