Parameter identification of inelastic structures under dynamic loads

Zhang, Haochuan; Foliente, Greg; Yang, Yongmin; Ma, Fai

doi:10.1002/eqe.151

Cited by 89 publications

(43 citation statements)

References 20 publications

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“…Reference [361] presents three algorithms based upon the simplex [24], extended Kalman filter [217], and generalized reduced gradient methods. The objective is to estimate the parameters of hysteresis for different classes of inelastic structures using the generalized Bouc-Wen model that accounts for degradation and pinching [17].…”

Section: Kalman Filter Based Identificationmentioning

confidence: 99%

The Hysteresis Bouc-Wen Model, a Survey

Ismail¹,

Ikhouane²,

Rodellar³

2009

Arch Computat Methods Eng

649

314

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Structural systems often show nonlinear behavior under severe excitations generated by natural hazards. In that condition, the restoring force becomes highly nonlinear showing significant hysteresis. The hereditary nature of this nonlinear restoring force indicates that the force cannot be described as a function of the instantaneous displacement and velocity. Accordingly, many hysteretic restoring force models were developed to include the time dependent nature using a set of differential equations. This survey contains a review of the past, recent developments and implementations of the Bouc-Wen model which is used extensively in modeling the hysteresis phenomenon in the dynamically excited nonlinear structures.

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Section: Kalman Filter Based Identificationmentioning

confidence: 99%

The Hysteresis Bouc-Wen Model, a Survey

Ismail¹,

Ikhouane²,

Rodellar³

2009

Arch Computat Methods Eng

649

314

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“…Using a parametric approach, several researchers have used a smooth hysteretic model for simulating and identifying hysteretic systems [22]. Yang et al [16] used NM to update the numerical model in their applications to hybrid simulation.…”

Section: Online Parameter Identification With Ukfmentioning

confidence: 99%

Implementation of online model updating in hybrid simulation

Hashemi

Masroor

Mosqueda

2013

Earthq Engng Struct Dyn

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SUMMARY Hybrid simulation combines numerical and experimental methods for cost‐effective, large‐scale testing of structures under simulated earthquake loading. Structural system level response can be obtained by expressing the equation of motion for the combined experimental and numerical substructures, and solved using time‐stepping integration similar to pure numerical simulations. It is often assumed that a reliable model exists for the numerical substructures while the experimental substructures correspond to parts of the structure that are difficult to model. A wealth of data becomes available during the simulation from the measured experiment response that can be used to improve upon the numerical models, particularly if a component with similar structural configuration and material properties is being tested and subjected to a comparable load pattern. To take advantage of experimental measurements, a new hybrid test framework is proposed with an updating scheme to update the initial modeling parameters of the numerical model based on the instantaneously‐measured response of the experimental substructures as the test progresses. Numerical simulations are first conducted to evaluate key algorithms for the selection and calibration of modeling parameters that can be updated. The framework is then expanded to conduct actual hybrid simulations of a structural frame model including a physical substructure in the laboratory and a numerical substructure that is updated during the tests. The effectiveness of the proposed framework is demonstrated for a simple frame structure but is extendable to more complex structural behavior and models. Copyright © 2013 John Wiley & Sons, Ltd.

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“…[5][6][7][8][9] Figure 1 compares the experimental results of a buckling-restrained brace (BRB) component test with the results of simulations with the 2-surface model. 12, 13 Zhang et al 12 showed that the generalized reduced gradient method is satisfactory in parameter identification of the modified Bouc-Wen model. 5,10 There are a total of 10 control parameters in the 2-surface model as listed in Table 1.…”

Section: Discussionmentioning

confidence: 99%

Parameter identification for on‐line model updating in hybrid simulations using a gradient‐based method

Chuang

Hsieh

Tsai

et al. 2017

Earthq Engng Struct Dyn

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Summary To improve the efficiency of model fitting, parameter identification techniques have been actively investigated. Recently, the applications of parameter identification migrated from off‐line model fitting to on‐line model updating. The objective of this study is to develop a gradient‐based method for model updating to advance hybrid simulation also called hybrid test. A novel modification of the proposed method, which can reduce the number of design variables to improve the identification efficiency, is illustrated in detail. To investigate the model updating, simulated hybrid tests were conducted with a 5‐story steel frame equipped with buckling‐restrained braces (BRBs) utilized in the shaking table tests conducted in E‐Defense in Japan in 2009. The calibrated analytical model that was verified with the test results can serve as the reference model. In the simulated hybrid tests, the physical BRB substructure is numerically simulated by utilizing a truss element with the 2‐surface model identical to the part of the reference model. Such numerical verification allows simulation of measurement errors for investigation on the performance of the proposed method. Moreover, the feasibility of sharing the identified parameter values, which were obtained from the physical substructure responses, with the relevant numerical models is also verified with the artificial component responses derived from the physical experiments.

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Parameter identification of inelastic structures under dynamic loads

Cited by 89 publications

References 20 publications

The Hysteresis Bouc-Wen Model, a Survey

The Hysteresis Bouc-Wen Model, a Survey

Implementation of online model updating in hybrid simulation

Parameter identification for on‐line model updating in hybrid simulations using a gradient‐based method

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