Neural network based approach for determining the shear strength of circular reinforced concrete columns

The objective of this study is to investigate the adequacy of neural networks (NN) as a quicker, more secure and more robust method to determine the shear strength of circular reinforced concrete columns. In the application of the NN model, a multilayer perceptron (MLP) with a back-propagation (BP)...

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Veröffentlicht in:Construction & building materials Jg. 23; H. 10; S. 3225 - 3232
1. Verfasser: Caglar, Naci
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.10.2009
Elsevier B.V
Schlagworte:
Algorithms
Back propagation
Building materials
Circular RC column
Construction materials
Matlab
Neural networks
Reinforced concrete
Scaled conjugate gradient algorithm
Shear strength
Shear strength
Scaled conjugate gradient algorithm
Neural networks
Circular RC column
ISSN:0950-0618, 1879-0526
Online-Zugang:Volltext
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Zusammenfassung:The objective of this study is to investigate the adequacy of neural networks (NN) as a quicker, more secure and more robust method to determine the shear strength of circular reinforced concrete columns. In the application of the NN model, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN model is developed, trained and tested through a based MATLAB program. The data used for training and testing NN model are gathered from literature. NN based model outputs are compared with ACI, ATC-32, ASCE and CALTRANS codes outcomes on the basis of the experimental results. This comparison demonstrated that the NN based model is highly successful to determine the shear strength of circular reinforced concrete columns.
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ISSN:0950-0618
1879-0526
DOI:10.1016/j.conbuildmat.2009.06.002