Determination of Damage in Reinforced Concrete Frames with Shear Walls Using Self-Organizing Feature Map
| Autor: | Faezehossadat Khademi, Łukasz Sadowski, Mehdi Nikoo, Mohammad Reza Nikoo |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
Article Subject Computer Networks and Communications Computer science business.industry 0211 other engineering and technologies Computational Mechanics 020101 civil engineering 02 engineering and technology Structural engineering Transfer function lcsh:QA75.5-76.95 0201 civil engineering Computer Science Applications Nonlinear system Artificial Intelligence 021105 building & construction Linear regression Feature (machine learning) Shear wall Radial basis function lcsh:Electronic computers. Computer science business Nonlinear regression Civil and Structural Engineering |
| Zdroj: | Applied Computational Intelligence and Soft Computing, Vol 2017 (2017) |
| ISSN: | 1687-9724 |
| DOI: | 10.1155/2017/3508189 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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