A comparison of sensitivity analyses for selected prestressed concrete structures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F18%3APU128809" target="_blank" >RIV/00216305:26110/18:PU128809 - isvavai.cz</a>

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/suco.201700291" target="_blank" >https://onlinelibrary.wiley.com/doi/abs/10.1002/suco.201700291</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/suco.201700291" target="_blank" >10.1002/suco.201700291</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A comparison of sensitivity analyses for selected prestressed concrete structures

Popis výsledku v původním jazyce

Three sensitivity analysis methods are employed to achieve the optimum selection of the dominant random variables of selected concrete structures. The first of these methods uses the nonparametric rank‐order statistical correlation between the basic random input variables and the structural response output variable. The second is neural network ensemble‐based sensitivity analysis and the last of them is sensitivity analysis in terms of coefficient of variation. All of the methods were utilized and compared for two selected concrete structures: a prestressed concrete bridge made of MPD girders, and T‐shaped prestressed concrete roof girder. The obtained information was used to set up a stochastic model and response surfaces in an optimum manner and was employed in the subsequent determination of selected uncertain design parameters followed by load‐bearing capacity and reliability assessment.

Název v anglickém jazyce

A comparison of sensitivity analyses for selected prestressed concrete structures

Popis výsledku anglicky

Three sensitivity analysis methods are employed to achieve the optimum selection of the dominant random variables of selected concrete structures. The first of these methods uses the nonparametric rank‐order statistical correlation between the basic random input variables and the structural response output variable. The second is neural network ensemble‐based sensitivity analysis and the last of them is sensitivity analysis in terms of coefficient of variation. All of the methods were utilized and compared for two selected concrete structures: a prestressed concrete bridge made of MPD girders, and T‐shaped prestressed concrete roof girder. The obtained information was used to set up a stochastic model and response surfaces in an optimum manner and was employed in the subsequent determination of selected uncertain design parameters followed by load‐bearing capacity and reliability assessment.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20102 - Construction engineering, Municipal and structural engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Structural Concrete

ISSN

1464-4177

e-ISSN

1751-7648

Svazek periodika

20

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

14

Strana od-do

38-51

Kód UT WoS článku

000459967500005

EID výsledku v databázi Scopus

2-s2.0-85062289810