Bayesian-based method for the simultaneous identification of structural damage and moving force. (15th February 2023)

• Record Type: Journal Article
• Authors: Chen, Zhidan; Weng, Shun; Yu, Hong; Li, Jiajing; Zhu, Hongping; Yan, Yongyi; Wu, Liying
• Is Part Of: Mechanical systems and signal processing. Volume 185(2023)
• Publication Date: 2023-02-15
• Subjects: Damage identification -- Moving force identification -- Bayesian method; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.109742
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A Bayesian method for the simultaneous identification of damage and moving force. A method for determining hyperparameters of damage and moving force is derived. The response sensitivity with damage parameters and force parameters are derived. The uncertainty of the damage and force parameters is quantified. Abstract: 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.