Reliability calculation of time-consuming problems using a small-sample artificial neural network-based response surface method

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F16%3APU120345" target="_blank" >RIV/00216305:26110/16:PU120345 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/s00521-016-2485-3" target="_blank" >http://dx.doi.org/10.1007/s00521-016-2485-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-016-2485-3" target="_blank" >10.1007/s00521-016-2485-3</a>

Result language

angličtina

Original language name

Reliability calculation of time-consuming problems using a small-sample artificial neural network-based response surface method

Original language description

An important step when designing and assessing the reliability of existing structures and/or structural elements is to calculate the reliability level described by failure probability or reliability index. Since calculating the structural response of complex systems such as bridges is usually a time-consuming task, the utilization of approximation methods with a view to reducing the computational effort to an acceptable level is an appropriate solution. The paper introduces a small-sample artificial neural network- based response surface method. An artificial neural net- work is used as an approximation (a so-called response surface) of the original limit state function. In order to be as effective as possible with respect to computational effort, a stratified Latin hypercube sampling simulation method is utilized to properly select training set elements. Subsequently, the artificial neural network-based response surface is utilized to calculate failure probability. To increase the accuracy of the determined failure probability, the response surface can be updated close to the failure region. This is performed by finding a new anchor point, which lies close to the design point of the limit state function. The new anchor point is then used to prepare the updated training set. The efficiency of the proposed method is tested for different training set sizes using a nonlinear limit state function taken from the literature, and the reliability assessment of three concrete bridges, one with explicit and two with implicit limit state functions in the form of finite element method models.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20102 - Construction engineering, Municipal and structural engineering

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2017

Confidentiality

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

Name of the periodical

NEURAL COMPUTING & APPLICATIONS

ISSN

0941-0643

e-ISSN

1433-3058

Volume of the periodical

28

Issue of the periodical within the volume

6

Country of publishing house

GB - UNITED KINGDOM

Number of pages

15

Pages from-to

1249-1263

UT code for WoS article

000403939000006

EID of the result in the Scopus database

2-s2.0-84978745352