Vibration load identification in the time-domain of high arch dam under discharge excitation based on hybrid LSQR algorithm. (1st September 2022)

• Record Type: Journal Article
• Authors: Li, Huokun; Liu, Bo; Huang, Wei; Liu, Hanyue; Wang, Gang
• Is Part Of: Mechanical systems and signal processing. Volume 177(2022)
• Publication Date: 2022-09-01
• Subjects: Load identification -- High arch dam -- Ill-posedness -- Tikhonov regularization -- LSQR algorithm; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.109193
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A hybrid LSQR algorithm for vibration source load identification of high arch dam is proposed. The ill posedness of load time-domain identification is further improved. The method can identify multiple vibration source load and has better antinoise performance. Abstract: The identification of vibration load is of great importance in studying the vibration induced by discharge of high arch dam. Considering the ill-posedness in time-domain identification of vibration load, a hybrid least squares QR (LSQR) iterative identification method is proposed. The system response is expressed as the convolution of the unit impulse response function and the excitation load. It is discretized into a set of linear equations, and the mathematical model of the inverse problem of load identification is established. On the basis of vibration signal filtering and noise reduction, Tikhonov regularization method is used to pre optimize LSQR iterative algorithm. This process is performed because the LSQR algorithm is prone to nonconvergence when the error of observation data is large. Thus, a hybrid LSQR algorithm is obtained to improve the ill-posedness of the inverse problem. Numerical examples show that the proposed method can recognize multiple vibration loads effectively and stably under different noise levels, and the recognition accuracy is better than the conventional regularization method. The proposed method is applied to an engineering example. Results show that the proposed method is feasible and effective for the time-domain identification of vibration load of a high arch dam.