Neural network ensemble-based sensitivity analysis in structural engineering: Comparison of selected methods and the influence of statistical correlation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F21%3APU137898" target="_blank" >RIV/00216305:26110/21:PU137898 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0045794920301796" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0045794920301796</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compstruc.2020.106376" target="_blank" >10.1016/j.compstruc.2020.106376</a>

Result language

angličtina

Original language name

Neural network ensemble-based sensitivity analysis in structural engineering: Comparison of selected methods and the influence of statistical correlation

Original language description

Surrogate model-based sensitivity analysis, especially framed by neural network ensemble (NNE), is an attractive but unresolved issue in structural reliability assessment. In this paper, differing from existing studies, an overview and assessment of typical methods for surrogate model-based parameter sensitivity analysis, namely the input perturbation method, the local analysis of variance, the connection weight method, the non-parametric Spearman rank-order correlation method, and the Sobol indices method, are performed and demonstrated on three illustrative cases of increasing complexity: a simple theoretical instance, an engineering case of midspan deflection of a simply-supported beam, and a real-world practical application of shear failing in a precast concrete girder. Through comprehensive comparisons, several findings are obtained as follows: (i) the NNE is testified a superior surrogate model for sensitivity analysis to a single artificial neural network; (ii) robustness and accuracy of an NNE in sensitivity analysis are demonstrated; (iii) the properties of these parameter sensitivity analysis methods are fully clarified with distinguished merits and limitations; (iv) mechanism of local- and global- sensitivity analysis methods is revealed; and (v) the strategy for sensitivity analysis of correlated descriptive variables are elaborated to address the impact of correlation among random variables in engineering systems. (C) 2020 Elsevier Ltd. All rights reserved.

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

2021

Confidentiality

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

Name of the periodical

COMPUTERS & STRUCTURES

ISSN

0045-7949

e-ISSN

1879-2243

Volume of the periodical

242

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

19

Pages from-to

1-19

UT code for WoS article

000580652800010

EID of the result in the Scopus database

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