A Copula‐Based and Monte Carlo Sampling Approach for Structural Dynamics Model Updating with Interval Uncertainty

Chen, Xueqian; Shen, Zhanpeng; Liu, Xin’en

doi:10.1155/2018/3958016

Cited by 3 publications

(4 citation statements)

References 29 publications

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“…In order to take into account the influence of each component accurately during the seismic evaluation, the joint probability distribution function of seismic demand for the components should be established. In this study, more consideration was given to the correlation between component failure states, so Monte Carlo sampling method [48,49], due to the strong ability to deal with nonlinear problems and high estimation accuracy [50,51], was used to develop the fragility of bridge system.…”

Section: Probabilistic Seismic Demand Modelmentioning

confidence: 99%

Elaborate Modeling and Fragility Assessment of a Multiframe PC Box‐Girder Bridge with Intermediate Hinges in California

Zhan²,

Liu

2021

Shock and Vibration

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Multiframe PC box-girder bridge with intermediate hinges is a kind of bridge having complex structural characters, which is very quintessential in California. In this study, a typical bridge was adopted to establish a nonlinear dynamic model through OpenSees platform. Intermediate hinge and inhibiting devices in it were elaborately simulated. Meanwhile, pushover analysis was used to reinstate a specimen of column test, which has the similar ratio of reinforcement to the typical bridge, and the hysteretic model parameters of the longitudinal steels inside columns were obtained. The damage indexes of column and hinge, which are primary components, under different limit states were acquired by moment-curvature analysis. Taking into account the uncertainty, nonlinear time-history analysis of the bridge was carried out through a suite of synthetic ground motions. Subsequently, a probabilistic seismic demand model was developed, and fragility curves were further focused on. According to fragility assessment, the conclusion shows that columns and hinge restrainers exhibit high fragility, and bridge system fragility is gradually determined by column fragility along with aggravating of the damage state. Unseating of girder can hardly occur at abutments and intermediate hinges. Moderate limit state could be exceeded in the positions of plug-type concrete structures in intermediate hinges, which tends to create transverse and vertical cracks, furthermore causing reinforcements yield. It indicates that it would severely underestimate the seismic fragility of intermediate hinges without considering the elaborate simulation of hinges.

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Section: Probabilistic Seismic Demand Modelmentioning

confidence: 99%

Elaborate Modeling and Fragility Assessment of a Multiframe PC Box‐Girder Bridge with Intermediate Hinges in California

Zhan²,

Liu

2021

Shock and Vibration

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“…Similarly, the unbiased estimation method is applied to obtain the unbiased intervals of the above sample set, which shown in Equation (30).…”

Section: Numerical Examplementioning

confidence: 99%

“…In recent years, the non-probabilistic method based on interval theory has been proposed to describe the uncertainty [29][30][31][32], which avoids the calculation of the probability density function and only needs the lower bound (LB) and upper bound (UB) of the sample interval. Khodaparast et al [33] presented a novel parameter identification method for structure system with the technology of sensitivity analysis of the Kriging meta-model.…”

Section: Introductionmentioning

confidence: 99%

Interval Identification of Thermal Parameters Using Trigonometric Series Surrogate Model and Unbiased Estimation Method

Wang

Zhao

2020

Applied Sciences

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Metal-foam materials have been applied in many engineering fields in virtue of its high specific strength and desirable of thermodynamic properties. However, due to the inherent uncertainty of its attribute parameters, reliable analysis results are often ambiguous to obtain accurately. To overcome this drawback, this paper proposes a novel interval parameter identification method. Firstly, a novel modelling methodology is proposed to simulate the geometry of engineering metal foams. Subsequently, the concept of intervals is introduced to represent the uncertainty relationship between variables and responses in heat transfer systems. To improve computational efficiency, a novel augmented trigonometric series surrogate model is constructed. Moreover, unbiased estimation methods based on different probability distributions are presented to describe system measurement intervals. Then, a multi-level optimization-based identification strategy is proposed to seek the parameter interval efficiently. Eventually, an engineering heat transfer system is given to verify the feasibility of the proposed parameter identification method. This method can rapidly identify the unknown parameters of the system. The identification results demonstrate that this interval parameter identification method can quantify the uncertainty of a metal-foam structure in engineering heat transfer systems efficiently, especially for the actual case without sufficient measurements.

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“…Wang and Matthies [22] put forward an FE model updating approach in structural dynamics with interval uncertain parameters based on copula and non-parameter kernel density estimation approach. Chen et al [23] constructed a nestedloop optimization procedure to perform interval parameter identification via Legendre polynomial chaos expansion and applied it to the parameter identification for engineering heat transfer systems. Besides, Wang et al [24] proposed a timedomain based distributed dynamic load identification method considering unknown-but-bounded uncertainties to implement high-precision identification of indeterminate models under the action of distributed dynamic load.…”

Section: Introductionmentioning

confidence: 99%

Novel Interval Parameter Identification Method Using Augmented Fourier Series-Based Polynomial Surrogate Model

Wang

Zhao

2019

IEEE Access

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Traditional probabilistic-based parameter identification methods cannot operate in practical engineering systems without sufficient available measurements. To overcome this drawback, a novel augmented Fourier polynomial-based interval parameter identification method is proposed in this paper. First, a novel augmented Fourier series-based polynomial surrogate model is established to precisely describe the mathematical relationship between the identified parameters and system responses. Subsequently, to improve the efficiency of the identification procedure, an improved interval-based multi-objective optimization strategy is designed to seek the nominal and fluctuation value of the parameter interval simultaneously. Eventually, the numerical and experimental examples are provided to verify the feasibility of the proposed method. The proposed method can achieve competitive results on various examples, the mass-spring system, and steel plate structure, compared with the previous methods. Moreover, satisfactory results are also obtained when applied to a space truss structure. The identification results demonstrate the feasibility and reliability of the proposed interval parameter identification method. INDEX TERMS Interval theory, parameter identification, augmented Fourier series-based surrogate model, multi-objective optimization strategy.

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A Copula‐Based and Monte Carlo Sampling Approach for Structural Dynamics Model Updating with Interval Uncertainty

Cited by 3 publications

References 29 publications

Elaborate Modeling and Fragility Assessment of a Multiframe PC Box‐Girder Bridge with Intermediate Hinges in California

Elaborate Modeling and Fragility Assessment of a Multiframe PC Box‐Girder Bridge with Intermediate Hinges in California

Interval Identification of Thermal Parameters Using Trigonometric Series Surrogate Model and Unbiased Estimation Method

Novel Interval Parameter Identification Method Using Augmented Fourier Series-Based Polynomial Surrogate Model

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