Utilization of artificial neural networks for global sensitivity analysis of model output
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F19%3APU131736" target="_blank" >RIV/00216305:26110/19:PU131736 - isvavai.cz</a>
Result on the web
<a href="https://aip.scitation.org/doi/abs/10.1063/1.5114107" target="_blank" >https://aip.scitation.org/doi/abs/10.1063/1.5114107</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1063/1.5114108" target="_blank" >10.1063/1.5114108</a>
Alternative languages
Result language
angličtina
Original language name
Utilization of artificial neural networks for global sensitivity analysis of model output
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20101 - Civil engineering
Result continuities
Project
<a href="/en/project/GA17-02862S" target="_blank" >GA17-02862S: Probabilistic modelling and optimization of shear strength of concrete beams (PROMOSS)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
AIP Conference Proceedings
ISBN
978-0-7354-1854-7
ISSN
0094-243X
e-ISSN
—
Number of pages
4
Pages from-to
„120005-1“-„120005-4“
Publisher name
American Institute of Physics Inc.
Place of publication
MELVILLE, USA
Event location
Ixia, Rhodes
Event date
Sep 13, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000521108600119