Bayesian-based method for the simultaneous identification of structural damage and moving force

被引：11

作者：

Chen Z. ^{[1
]}

Weng S. ^{[1
]}

Yu H. ^{[2
]}

Li J. ^{[3
]}

Zhu H. ^{[1
]}

Yan Y. ^{[2
]}

Wu L. ^{[1
]}

机构：

[1] School of Civil Engineering and Mechanics, Huazhong University of Science and Technology

[2] School of Civil Engineering and Architecture, Wuhan Institute of Technology

来源：

Mechanical Systems and Signal Processing | 2023年 / 185卷

基金：

中国国家自然科学基金;

关键词：

Bayesian method; Damage identification; Moving force identification;

D O I：

10.1016/j.ymssp.2022.109742

中图分类号：

学科分类号：

摘要：

The input force is usually difficult to measure directly in the damage identification due to the system uncertainty and noise uncertainty. A Bayesian method for the simultaneous identification of damage and moving force is proposed. The moving force on the structure are transformed into equivalent nodal forces through the element function, and these equivalent nodal forces are expanded by using Chebyshev orthogonal polynomials. The posterior probability density function of the damage parameter and the force orthogonal parameter under Bayesian theory is deduced in time-domain. The prior information of the Chebyshev orthogonal polynomials adopts the uniform prior, and the damage parameters adopt the Gaussian prior in accordance with the characteristics of each parameter. The Laplace approximation method for time domain data is used to determine the hyperparameters of elemental damage and moving force. The first-order and second-order sensitivities of the dynamic response with damage and force parameters are derived to update the hyperparameters and to accelerate gradient-based system identification. The uncertainty of the identified results of damage parameters is quantified by using an approximate Gaussian distribution. Two numerical cases of a simply supported beam and the Tanjiang Bridge on the Shen-Mao Railway show that the proposed method can simultaneously identify the elemental damage and moving forces with uncertainty quantified. © 2022

引用

共 33 条

[21]

Tipping M.E., Sparse Bayesian learning and the relevance vector machine, J. Mach. Learn. Res., 1, pp. 211-244, (2001)

[22]

Tipping M.E., Faul A.C., Fast marginal likelihood maximisation for sparse Bayesian models, Proc. Ninth Int. Workshop Artif. Intell. Statistics, 4, pp. 276-283, (2003)

[23]

Hou R., Xia Y., Zhou X., Huang Y., Sparse Bayesian learning for structural damage detection using expectation–maximization technique, Struct. Control Health. Monit., 26, (2019)

[24]

Wang X., Hou R., Xia Y., Zhou X., Laplace approximation in sparse Bayesian learning for structural damage detection, Mech. Syst. Signal Process., 140, (2020)

[25]

Zhu H., Li J., Tian W., Weng S., Peng Y., Zhang Z., Chen Z., An enhanced substructure-based response sensitivity method for finite element model updating of large-scale structures, Mech. Syst. Signal Process., 154, (2021)

[26]

Zhu H., Yu H., Gao F., Weng S., Sun Y., Hu Q., Damage identification using time series analysis and sparse regularization, Struct. Control Health. Monit., 27, (2020)

[27]

Weng S., Xia Y., Xu Y.-L., Zhu H.-P., Substructure based approach to finite element model updating, Comput. Struct., 89, pp. 772-782, (2011)

[28]

Jaynes E.T., Prior Probabilities, IEEE Trans. Syst. Man. Cybern., 4, pp. 227-241, (1968)

[29]

Byrd R.H., Gilbert J.C., Nocedal J., A trust region method based on interior point techniques for nonlinear programming, Math. Program., 89, pp. 149-185, (2000)

[30]

Azevedo-Filho A., Shachter R.D.

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