Prestressed concrete roof girders: Part II – Surrogate modeling and sensitivity analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F18%3APU130097" target="_blank" >RIV/00216305:26110/18:PU130097 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Prestressed concrete roof girders: Part II – Surrogate modeling and sensitivity analysis

Popis výsledku v původním jazyce

The paper describes a particular part of complex stochastic modeling and design of a precast prestressed concrete girder failing in shear, namely surrogate modeling and sensitivity analysis. Both methods were efficiently used in order to reduce high computational effort related to utilization of a 3D nonlinear FEM model. Two types of surrogate models have been developed: (1) artificial neural network model and (2) polynomial chaos expansion model. In case of sensitivity analysis, three methods were utilized and compared: (i) Spearman non-parametric rank-order statistical correlation sensitivity, (ii) sensitivity analysis in terms of coefficient of variation, and (iii) sensitivity analysis in terms of Sobol sensitivity indices. The obtained information was used to set up a stochastic model and surrogate models in an optimum manner and was employed in the subsequent determination of selected uncertain design parameters followed by load-bearing capacity and reliability assessment using semi-probabilistic as well as fully probabilistic approaches.

Název v anglickém jazyce

Prestressed concrete roof girders: Part II – Surrogate modeling and sensitivity analysis

Popis výsledku anglicky

The paper describes a particular part of complex stochastic modeling and design of a precast prestressed concrete girder failing in shear, namely surrogate modeling and sensitivity analysis. Both methods were efficiently used in order to reduce high computational effort related to utilization of a 3D nonlinear FEM model. Two types of surrogate models have been developed: (1) artificial neural network model and (2) polynomial chaos expansion model. In case of sensitivity analysis, three methods were utilized and compared: (i) Spearman non-parametric rank-order statistical correlation sensitivity, (ii) sensitivity analysis in terms of coefficient of variation, and (iii) sensitivity analysis in terms of Sobol sensitivity indices. The obtained information was used to set up a stochastic model and surrogate models in an optimum manner and was employed in the subsequent determination of selected uncertain design parameters followed by load-bearing capacity and reliability assessment using semi-probabilistic as well as fully probabilistic approaches.

Druh

D - Stať ve sborníku

CEP obor

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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

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Life Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision: Proceedings of the Sixth International Symposium on Life-Cycle Civil Engineering (IALCCE 2018)

ISBN

9781138626331

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

495-502

Název nakladatele

CRC press, Taylor and Francis group

Místo vydání

London

Místo konání akce

Ghent

Datum konání akce

28. 10. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000471120403026