Detail publikace

Utilization of artificial neural networks for global sensitivity analysis of model output

KALA, Z. LEHKÝ, D. NOVÁK, D.

Originální název

Utilization of artificial neural networks for global sensitivity analysis of model output

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

The paper deals with the application of artificial neural networks to sensitivity measurement of the output quantity to the variability of input quantities. The original nonlinear FEM model calculates ultimate load-bearing capacity of a T-shaped prestressed concrete roof girder. Latin hypercube sampling algorithm is used to generate samples of input variables. The global Sobol sensitivity analysis is proposed to understand the effect of the input variability on the quantity of interest. The outputs of the Sobol sensitivity analysis are verified by subsequent two sensitivity analyses. The first studies show that artificial neural networks are very promising for effective evaluation of global sensitivity analysis. Artificial neural networks do not eliminate mutual interaction among input quantities; it is a very important piece of knowledge connected with maintaining the satisfactory accurateness of the reliability computation.

Klíčová slova

artificial neural networks, Sobol, global sensitivity analysis, interactions, reliability

Autoři

KALA, Z.; LEHKÝ, D.; NOVÁK, D.

Vydáno

24. 7. 2019

Nakladatel

American Institute of Physics Inc.

Místo

MELVILLE, USA

ISBN

978-0-7354-1854-7

Kniha

AIP Conference Proceedings

ISSN

0094-243X

Periodikum

AIP conference proceedings

Ročník

2116

Stát

Spojené státy americké

Strany od

120005-1

Strany do

120005-4

Strany počet

4

URL