Implementation of online model updating in hybrid simulation

Hashemi, Javad; Masroor, A.; Mosqueda, Gilberto

doi:10.1002/eqe.2350

Cited by 44 publications

(46 citation statements)

References 19 publications

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“…This result indicates the attractive benefit offered by RTHSMU which improves the RTHS accuracy without the need for testing all instances of components with modeling uncertainties. This general finding has also been reported in [22,42,16,26].…”

Section: Simulation Case Studysupporting

confidence: 77%

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Real time hybrid simulation with online model updating: An analysis of accuracy

Dyke

Prakash

2017

Mechanical Systems and Signal Processing

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a b s t r a c tIn conventional hybrid simulation (HS) and real time hybrid simulation (RTHS) applications, the information exchanged between the experimental substructure and numerical substructure is typically restricted to the interface boundary conditions (force, displacement, acceleration, etc.). With additional demands being placed on RTHS and recent advances in recursive system identification techniques, an opportunity arises to improve the fidelity by extracting information from the experimental substructure. Online model updating algorithms enable the numerical model of components (herein named the target model), that are similar to the physical specimen to be modified accordingly. This manuscript demonstrates the power of integrating a model updating algorithm into RTHS (RTHSMU) and explores the possible challenges of this approach through a practical simulation. Two Bouc-Wen models with varying levels of complexity are used as target models to validate the concept and evaluate the performance of this approach. The constrained unscented Kalman filter (CUKF) is selected for using in the model updating algorithm. The accuracy of RTHSMU is evaluated through an estimation output error indicator, a model updating output error indicator, and a system identification error indicator. The results illustrate that, under applicable constraints, by integrating model updating into RTHS, the global response accuracy can be improved when the target model is unknown. A discussion on model updating parameter sensitivity to updating accuracy is also presented to provide guidance for potential users.

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Section: Simulation Case Studysupporting

confidence: 77%

“…The results indicate that the UKF can be used effectively for nonlinear structural identification, and further extensions consider experimental validation in [37]. This approach continues to be recognized as being effective for online HS applications [16,26].…”

Section: Constrained Unscented Kalman Filter Algorithmmentioning

confidence: 98%

Real time hybrid simulation with online model updating: An analysis of accuracy

Dyke

Prakash

2017

Mechanical Systems and Signal Processing

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“…During the hybrid simulation procedure, the physical specimen provides a valuable amount of information, which is traditionally analyzed after finishing the experiment. Model updating aims to utilize this information during the test through identifying some action-deformation properties from the physical component, in order to modify the behavior of the corresponding numerical parts [Hashemi et al, 2014]. The success of this approach requires that the source and the modified components share close characteristics [Elanwar and Elnashai, 2014].…”

Section: Introductionmentioning

confidence: 99%

Framework for Online Model Updating in Earthquake Hybrid Simulations

Elanwar

Elnashai

2015

Journal of Earthquake Engineering

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Hybrid simulation has been effectively utilized to assess structural response subjected to intense dynamic loads. The process comprises dividing the structure into experimental and numerical modules. The experimental modules represent the critical components responses, which cannot be idealized reliably through analytical approaches. The responses of the different modules are combined through a stepwise integration scheme. In conventional hybrid simulations, the number of experimental components is restricted by the capacity of the test facility; usually 1-3 components, and the numerical simulation does not benefit from the information acquired from the tested component during the analysis. In this article, a framework is proposed to identify the material constitutive relationship from the tested component(s) and to update the corresponding numerical parts that share close characteristics with the physical tests. Optimization tools and neural networks are presented as alternatives for the identification procedure; the framework is however extendable and scalable. The communication protocol between the different structural components is also discussed within the proposed framework. Several analytical examples are presented to prove the feasibility of the presented framework, while experiments are used to verify the process in a companion article.

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“…Hence, this approach has been conventionally used to evaluate structures including few components that show intense nonlinear behavior [Hashemi et al, 2014]. Hybrid simulation excel quasi-static experiments as it takes into consideration the inter-dependency between the experimental and numerical modules during the analysis [Mahin and Shing, 1985].…”

Section: Introductionmentioning

confidence: 99%

Application of In-Test Model Updating to Earthquake Structural Assessment

Elanwar

Elnashai

2015

Journal of Earthquake Engineering

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Analytical methods are frequently utilized for structural assessment due to their simplicity and costeffectiveness. However, modeling of material inelasticity and geometric nonlinearity under reversed inelastic deformations is still very challenging and its accuracy is difficult to quantify. On the other hand, realistic experimental assessment is costly, time-consuming, and impractical for large or spatially extended structures. Hybrid simulation has been developed as an approach that combines the realism of experimental techniques with the economy of analytical tools. In hybrid simulation, the structural is divided into several modules such that the critical components are tested in the laboratory, while the rest of the structure is simulated numerically. The equations of motion solved in the computer enable the integration of the analytical and experimental components at each time increment. The objective of this article is to apply a newly developed identification and model updating scheme to acquire the material constitutive relationship from the physically tested specimen during the analysis to two complex hybrid simulation case studies. The identification scheme is developed and verified in a companion article, while the two experiments presented in this article are selected such that they address different structural engineering applications. First, a beam-column steel connection with heat treated beam section is analyzed. Afterwards, the response of a multi-bay concrete bridge is investigated. The results of these two examples demonstrate the effectiveness of model updating to improve the numerical model response as compared to the conventional hybrid simulation approaches.

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Implementation of online model updating in hybrid simulation

Cited by 44 publications

References 19 publications

Real time hybrid simulation with online model updating: An analysis of accuracy

Real time hybrid simulation with online model updating: An analysis of accuracy

Framework for Online Model Updating in Earthquake Hybrid Simulations

Application of In-Test Model Updating to Earthquake Structural Assessment

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