Neural networks in 3-dimensional dynamic analysis of reinforced concrete buildings

The objective of this study is to investigate the adequacy of Artificial Neural Networks (ANN) as a securer, quicker and more robust method to determine the dynamic response of buildings in 3D. For this purpose, two ANN models were proposed to estimate the fundamental periods, base shear force, base...

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Vydáno v:	Construction & building materials Ročník 22; číslo 5; s. 788 - 800
Hlavní autoři:	Caglar, Naci, Elmas, Muzaffer, Yaman, Zeynep Dere, Saribiyik, Mehmet
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.05.2008 Elsevier B.V
Témata:	Adequacy Algorithms Analysis Artificial neural networks Back propagation Bays Bending moments Building materials industry Buildings Concrete construction Conjugate gradients Construction Displacement Dynamic analysis Dynamic response Dynamic tests Dynamics Earthquake construction Estimates Finite element method Learning theory Mathematical analysis Mathematical models Matlab Moments of inertia Multilayer perceptrons Neural networks Reinforced concrete Response of buildings Scaled conjugate gradient algorithm Seismic phenomena Shear Three dimensional Training Scaled conjugate gradient algorithm Neural networks Response of buildings Dynamic analysis
ISSN:	0950-0618, 1879-0526
On-line přístup:	Získat plný text
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