A utilization of the inverse response surface method for the reliability-based design of structures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F22%3APU145235" target="_blank" >RIV/00216305:26110/22:PU145235 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s00521-022-07149-w" target="_blank" >https://link.springer.com/article/10.1007/s00521-022-07149-w</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-022-07149-w" target="_blank" >10.1007/s00521-022-07149-w</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A utilization of the inverse response surface method for the reliability-based design of structures

Popis výsledku v původním jazyce

The paper discusses the pitfalls of using response surface methods when solving inverse problems and presents an adaptive artificial neural network-based inverse response surface method. The procedure is based on a coupling of the adaptive response surface method and artificial neural network-based inverse reliability analysis. The validity and accuracy of the method are tested on several examples. The first is a problem with a theoretical explicit nonlinear limit state function and one design parameter. Here, the accuracy of surrogate models for design parameter identification was tested for cases with the target values of the identified parameter both inside and outside of the initial range of values. The absolute percentage errors were 11.79 % and 0.19 % after the first and the last iteration of the identification process, respectively. The other two examples represent practical applications of the reliability design of structures with multiple design parameters and multiple reliability constraints. In the former, the limit state functions are defined explicitly, while in the latter, they are defined implicitly in the form of a structural analysis using the nonlinear finite element method. When assessing the reliability index values, very low absolute percentage error values were obtained in both examples. For the explicit form of the limit state function, the values were up to 0.50 % in all iterations. In the case of the implicitly defined limit state function, the absolute percentage error was equal to 6.45 % after the fist iteration and 0.79 % after the second iteration.

Název v anglickém jazyce

A utilization of the inverse response surface method for the reliability-based design of structures

Popis výsledku anglicky

The paper discusses the pitfalls of using response surface methods when solving inverse problems and presents an adaptive artificial neural network-based inverse response surface method. The procedure is based on a coupling of the adaptive response surface method and artificial neural network-based inverse reliability analysis. The validity and accuracy of the method are tested on several examples. The first is a problem with a theoretical explicit nonlinear limit state function and one design parameter. Here, the accuracy of surrogate models for design parameter identification was tested for cases with the target values of the identified parameter both inside and outside of the initial range of values. The absolute percentage errors were 11.79 % and 0.19 % after the first and the last iteration of the identification process, respectively. The other two examples represent practical applications of the reliability design of structures with multiple design parameters and multiple reliability constraints. In the former, the limit state functions are defined explicitly, while in the latter, they are defined implicitly in the form of a structural analysis using the nonlinear finite element method. When assessing the reliability index values, very low absolute percentage error values were obtained in both examples. For the explicit form of the limit state function, the values were up to 0.50 % in all iterations. In the case of the implicitly defined limit state function, the absolute percentage error was equal to 6.45 % after the fist iteration and 0.79 % after the second iteration.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20102 - Construction engineering, Municipal and structural engineering

Projekt

<a href="/cs/project/GA20-01734S" target="_blank" >GA20-01734S: Pravděpodobnostně orientovaná globální citlivostní měření konstrukční spolehlivosti</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

NEURAL COMPUTING & APPLICATIONS

ISSN

0941-0643

e-ISSN

1433-3058

Svazek periodika

34

Číslo periodika v rámci svazku

15

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

12845-12859

Kód UT WoS článku

000772273700002

EID výsledku v databázi Scopus

2-s2.0-85126906207