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

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.

[1]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[2]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[3]  Geert De Schutter,et al.  Damage to Concrete Structures , 2012 .

[4]  Andrei M. Reinhorn,et al.  IDARC2D, Version 4.0: A Computer Program for the Inelastic Damage Analysis of Buildings , 1996 .

[5]  Mehdi Nikoo,et al.  Determining Confidence for Evaluation of Vulnerability In Reinforced Concrete Frames with Shear Wall , 2012 .

[6]  Raffaele Landolfo,et al.  Prediction of the flexural overstrength factor for steel beams using artificial neural network , 2014 .

[7]  Łukasz Sadowski,et al.  Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks , 2015 .

[8]  Mehdi Nikoo,et al.  Determining Displacement in Concrete Reinforcement Building with using Evolutionary Artificial Neural Networks , 2012 .

[9]  Kasım Mermerdaş,et al.  Assessment of shear capacity of adhesive anchors for structures using neural network based model , 2016 .

[10]  Mehdi Nikoo,et al.  Determination of compressive strength of concrete using Self Organization Feature Map (SOFM) , 2013, Engineering with Computers.

[11]  Mehdi Nikoo,et al.  Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete , 2017, Frontiers of Structural and Civil Engineering.

[12]  Faezehossadat Khademi,et al.  Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression , 2016 .

[13]  Zheng Niu,et al.  Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification , 2004, Future Gener. Comput. Syst..

[14]  Y. J. Park,et al.  IDARC: Inelastic Damage Analysis of Reinforced Concrete Frame - Shear-Wall Structures , 1987 .

[15]  Mehdi Nikoo,et al.  Principal Component Analysis combined with a Self Organization Feature Map to determine the pull-off adhesion between concrete layers , 2015 .