Substructure on-Line Test Technique Using Real-Time Hysteresis Modeling With Neural Network

Yang, Wonjik; Nakano, Yoshiaki; Yamauchi, Naruhito; Sanada, Yasushi

doi:10.3130/aijs.70.89

Cited by 6 publications

(2 citation statements)

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“…In model updating, it is required that the numerical substructure has similar dynamic properties to the physical substructure, and the accuracy of the numerical substructure model is improved by loading the physical substructure and recording its dynamic properties to modify the numerical substructure model. In 2005, Yang and Nakano 45 proposed a model‐updating algorithm for RTHS based on the nonlinear hysteresis property of the neural network prediction structure, which was the first application of model updating to RTHS. However, the existence of bias in the method for predicting structural properties using neural networks cannot be avoided.…”

Section: Strategies Of Numerical Simulation In Rthsmentioning

confidence: 99%

State of the art and development trends in numerical simulation for real‐time hybrid simulation

Xiang

Tang

2022

Earthquake Engineering and Resilience

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Real-time hybrid simulation (RTHS) is a novel testing method that combines numerical simulation and physical testing to evaluate the dynamic performance of engineering structures in large specimens using existing experimental facilities, thus eliminating the scaled effect. In recent years, studies on RTHS have expanded beyond traditional engineering structures, such as wind turbines, automobiles, and ocean structures. Thus far, the dimensions of physical substructures in RTHS are still relatively small, limited by the accuracy of system control for physical systems and the real-time calculation efficiency of numerical simulations. This study summarizes the achievements of real-time numerical simulations for RTHS, including the dimensions of the physical specimen, strategy of numerical simulation, and integral algorithms of RTHS. Meanwhile, two theoretically feasible methods based on parallel computing and surrogate models are discussed, which may be the development trends needed to improve the efficiency and accuracy of numerical solutions in real-time and enlarge the application scope of RTHS.

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Section: Strategies Of Numerical Simulation In Rthsmentioning

confidence: 99%

State of the art and development trends in numerical simulation for real‐time hybrid simulation

Xiang

Tang

2022

Earthquake Engineering and Resilience

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show abstract

“…The first attempt of this technique was made by Yang et al [13], who employed a neural network for the numerical substructure and trained or, in other words, updated, the network based on online experimental data. In the modeling of civil engineering structures, constitutive models that can be analytically expressed appear more popular than black-box models such as neural networks.…”

Section: Introductionmentioning

confidence: 99%

Hybrid simulation of steel frame structures with sectional model updating

Chen

et al. 2016

Earthq Engng Struct Dyn

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SUMMARYHybrid simulations that combine numerical computations and physical experiment represent an effective method of evaluating the dynamic response of structures. However, it is sometimes impossible to take all the uncertain or nonlinear parts of the structure as the physical substructure. Thus, the modeling errors of the numerical part can raise concerns. One method of solving this problem is to update the numerical model by estimating its parameters from experimental data online. In this paper, an online model updating method for the hybrid simulation of frame structures is proposed to reduce the errors of nonlinear modeling of numerical substructures. To obtain acceptable accuracy with acceptable extra computation efforts as a result of model parameter estimation, the sectional constitutive model is adopted, therein considering axial-force and bending-moment coupling; moreover, the unscented Kalman filter is used for parameter estimation of the sectional model. The effectiveness of the sectional model updating with the unscented Kalman filter is validated via numerical analyses and actual hybrid tests on a full-scale steel frame structure, with one column as the experimental substructure loaded by three actuators to guarantee the consistency of the boundary conditions.

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Online numerical simulation method based on adaptive unscented Kalman filter for shear wall structures

Jiang,

2024

Engineering Structures

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Substructure on-Line Test Technique Using Real-Time Hysteresis Modeling With Neural Network

Abstract: In general， hysteresis models that are applied to

Cited by 6 publications

References 1 publication

State of the art and development trends in numerical simulation for real‐time hybrid simulation

State of the art and development trends in numerical simulation for real‐time hybrid simulation

Hybrid simulation of steel frame structures with sectional model updating

Online numerical simulation method based on adaptive unscented Kalman filter for shear wall structures

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