A global sensitivity analysis framework for hybrid simulation with stochastic substructures

Tsokanas, Nikolaos; Zhu, Xiao Feng; Abbiati, Giuseppe; Marelli, Stefano; Sudret, Bruno; Stojadinović, Božidar

doi:10.31224/osf.io/kjnec

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…32 There has been considerable engineering practice in traditional meta-models for the uncertainty quantification in hybrid simulation. Tsokanas et al 33 used the lambda surrogate model to replace the hybrid simulation response and confirmed the effectiveness of the proposed hybrid simulation global sensitivity analysis framework. Ligeikis and Christenson 34 combines real-time hybrid simulation (RTHS) and Kriging to metamodel the frequency response functions of a two degree-of-freedom mass-spring system to estimate distributions of the system response.…”

mentioning

confidence: 81%

A cross validation‐Voronoi and entropy based adaptive sampling strategy for uncertainty quantification through hybrid simulation

Chen

Hou

et al. 2022

Earthquake Engineering and Resilience

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Intensive and complex engineering models drive the need for computational affordability and accuracy. To obtain more accurate prediction, an effective sampling method is proposed and evaluated in this study by integrating CV (cross validation)-Voronoi (CV-V) and entropy (EP) strategies. The new CVV (CV-V) -DEP (Distance and EP) method uses CV-V to divide the sample space into Voronoi units and selects the unit with largest error. The candidate sample point with the largest EP and distance values in the selected unit is then selected. To verify the efficacy of the proposed method, CVV-DEP is first compared with traditional CV-V and CVV-EP (CV-V and EP) strategies for six nonlinear functions. Computational simulations are further conducted using the proposed method for uncertainty quantification through hybrid simulation of two different nonlinear systems. The proposed method is demonstrated to provide great potential for uncertainty quantification of civil engineering infrastructures through hybrid simulation.

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mentioning

confidence: 81%

A cross validation‐Voronoi and entropy based adaptive sampling strategy for uncertainty quantification through hybrid simulation

Chen

Hou

et al. 2022

Earthquake Engineering and Resilience

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“…Chen et al 49 used data-driven arbitrary chaotic polynomial analysis to account for the impact of structural parameter uncertainty under stochastic ground motions. Tsokanas et al 50 proposed a hybrid simulation global sensitivity analysis framework using a generalized lambda surrogate model. 51 combines RTHS and Kriging to metamodel the frequency response functions of a two degree-of-freedom mass-spring system to estimate distributions of the system response.…”

Section: Introductionmentioning

confidence: 99%

Real‐time hybrid simulation with multi‐fidelity Co‐Kriging for global response prediction under structural uncertainties

Chen

Yang

Hou

et al. 2022

Earthq Engng Struct Dyn

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Real‐time hybrid simulation (RTHS) provides an effective and efficient experimental technique to enable large‐ or full‐scale experiments to account for rate‐dependent behavior in size limited laboratories. Traditional practice of RTHS assumes deterministic substructure properties therefore could not account for structural uncertainties in global response prediction. This study explores the use of Co‐Kriging metamodeling for global response prediction under the presence of structural uncertainties. RTHS in laboratory is used as high‐fidelity (HF) modeling while computational simulation of the prototype structure under investigation is used as low‐fidelity (LF) modeling. Multifidelity modeling is integrated through Co‐Kriging to render accurate response prediction over the entire sample space of structural uncertainties. An entropy‐based adaptive strategy is used to sequentially determine the sampling points for HF RTHS and LF simulation. The proposed multifidelity Co‐Kriging approach is experimentally evaluated through RTHS of a single‐degree‐of‐freedom structure with a self‐centering viscous damper. The Co‐Kriging predicted maximum structural responses are further compared with validation tests. It is demonstrated that the Co‐Kriging can effectively reduce the number of RTHS tests in laboratory and significantly improve the metamodel accuracy for global prediction of structural response under uncertainties. The presented study presents an innovative way to further expand the capacity of RTHS for uncertainty quantification toward seismic hazard mitigation.

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“…In this regard, surrogate modeling has been proposed to perform global sensitivity analysis (GSA) of a quantity of interest (QoI) hybrid model response with respect to a set of input parameters originating from both substructures and excitation [11,12]. GSA aims to quantitatively determine the degree each input parameter affects the selected QoI of the hybrid model response.…”

Section: Introductionmentioning

confidence: 99%

A comparison of surrogate modeling techniques for global sensitivity analysis in hybrid simulation

Tsokanas¹,

Pastorino²,

Stojadinović³

2021

Preprint

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Hybrid simulation is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario. The system under consideration is divided into multiple individual loading-rate-sensitive substructures, out of which one or more are tested physically, whereas the remaining are simulated numerically. The coupling of all substructures forms the so-called hybrid model. Although hybrid simulation has been extensively used across various engineering disciplines, it is often the case that the hybrid model and related excitation is conceived as deterministic. However, associated uncertainties are present whilst simulation deviation due to their presence could be significant. To this regard, global sensitivity analysis based on Sobol' indices can be used to determine the sensitivity of the hybrid model response due to the presence of the associated uncertainties. Nonetheless, estimation of the Sobol' sensitivity indices requires unaffordable amount of hybrid simulation evaluations. Therefore, surrogate modeling techniques are used to alleviate this burden. In this paper, three different surrogate modeling methods are examined, namely polynomial chaos expansion, Kriging and polynomial chaos Kriging. A case study encompassing a virtual hybrid model is employed and hybrid model response quantities of interest are selected. Their respective surrogates are developed using all three aforementioned techniques. The Sobol' indices obtained utilizing each examined surrogate are compared with each other and results highlight potential deviations.

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A global sensitivity analysis framework for hybrid simulation with stochastic substructures

Cited by 4 publications

References 22 publications

A cross validation‐Voronoi and entropy based adaptive sampling strategy for uncertainty quantification through hybrid simulation

A cross validation‐Voronoi and entropy based adaptive sampling strategy for uncertainty quantification through hybrid simulation

Real‐time hybrid simulation with multi‐fidelity Co‐Kriging for global response prediction under structural uncertainties

A comparison of surrogate modeling techniques for global sensitivity analysis in hybrid simulation

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