A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures

Hybrid simulation is an experimental method used to investigate the dynamic response of a reference prototype structure by decomposing it to physically-tested and numerically-simulated substructures. The latter substructures interact with each other in a real-time feedback loop and their coupling fo...

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Autores principales:	Nikolaos Tsokanas, Xujia Zhu, Giuseppe Abbiati, Stefano Marelli, Bruno Sudret, Božidar Stojadinović
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	hybrid simulation stochastic hybrid model global sensitivity analysis (GSA) Sobol’ indices stochastic surrogate modeling generalized lambda model Engineering (General). Civil engineering (General) TA1-2040 City planning HT165.5-169.9
Acceso en línea:	https://doaj.org/article/f7614b0dc5344daba7c972b109da0067
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spelling	oai:doaj.org-article:f7614b0dc5344daba7c972b109da00672021-12-01T23:38:23ZA Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures2297-336210.3389/fbuil.2021.778716https://doaj.org/article/f7614b0dc5344daba7c972b109da00672021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fbuil.2021.778716/fullhttps://doaj.org/toc/2297-3362Hybrid simulation is an experimental method used to investigate the dynamic response of a reference prototype structure by decomposing it to physically-tested and numerically-simulated substructures. The latter substructures interact with each other in a real-time feedback loop and their coupling forms the hybrid model. In this study, we extend our previous work on metamodel-based sensitivity analysis of deterministic hybrid models to the practically more relevant case of stochastic hybrid models. The aim is to cover a more realistic situation where the physical substructure response is not deterministic, as nominally identical specimens are, in practice, never actually identical. A generalized lambda surrogate model recently developed by some of the authors is proposed to surrogate the hybrid model response, and Sobol’ sensitivity indices are computed for substructure quantity of interest response quantiles. Normally, several repetitions of every single sample of the inputs parameters would be required to replicate the response of a stochastic hybrid model. In this regard, a great advantage of the proposed framework is that the generalized lambda surrogate model does not require repeated evaluations of the same sample. The effectiveness of the proposed hybrid simulation global sensitivity analysis framework is demonstrated using an experiment.Nikolaos TsokanasXujia ZhuGiuseppe AbbiatiStefano MarelliBruno SudretBožidar StojadinovićFrontiers Media S.A.articlehybrid simulationstochastic hybrid modelglobal sensitivity analysis (GSA)Sobol’ indicesstochastic surrogate modelinggeneralized lambda modelEngineering (General). Civil engineering (General)TA1-2040City planningHT165.5-169.9ENFrontiers in Built Environment, Vol 7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hybrid simulation stochastic hybrid model global sensitivity analysis (GSA) Sobol’ indices stochastic surrogate modeling generalized lambda model Engineering (General). Civil engineering (General) TA1-2040 City planning HT165.5-169.9
spellingShingle	hybrid simulation stochastic hybrid model global sensitivity analysis (GSA) Sobol’ indices stochastic surrogate modeling generalized lambda model Engineering (General). Civil engineering (General) TA1-2040 City planning HT165.5-169.9 Nikolaos Tsokanas Xujia Zhu Giuseppe Abbiati Stefano Marelli Bruno Sudret Božidar Stojadinović A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures
description	Hybrid simulation is an experimental method used to investigate the dynamic response of a reference prototype structure by decomposing it to physically-tested and numerically-simulated substructures. The latter substructures interact with each other in a real-time feedback loop and their coupling forms the hybrid model. In this study, we extend our previous work on metamodel-based sensitivity analysis of deterministic hybrid models to the practically more relevant case of stochastic hybrid models. The aim is to cover a more realistic situation where the physical substructure response is not deterministic, as nominally identical specimens are, in practice, never actually identical. A generalized lambda surrogate model recently developed by some of the authors is proposed to surrogate the hybrid model response, and Sobol’ sensitivity indices are computed for substructure quantity of interest response quantiles. Normally, several repetitions of every single sample of the inputs parameters would be required to replicate the response of a stochastic hybrid model. In this regard, a great advantage of the proposed framework is that the generalized lambda surrogate model does not require repeated evaluations of the same sample. The effectiveness of the proposed hybrid simulation global sensitivity analysis framework is demonstrated using an experiment.
format	article
author	Nikolaos Tsokanas Xujia Zhu Giuseppe Abbiati Stefano Marelli Bruno Sudret Božidar Stojadinović
author_facet	Nikolaos Tsokanas Xujia Zhu Giuseppe Abbiati Stefano Marelli Bruno Sudret Božidar Stojadinović
author_sort	Nikolaos Tsokanas
title	A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures
title_short	A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures
title_full	A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures
title_fullStr	A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures
title_full_unstemmed	A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures
title_sort	global sensitivity analysis framework for hybrid simulation with stochastic substructures
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/f7614b0dc5344daba7c972b109da0067
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A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures

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