A new improved fractional Tikhonov regularization method for moving force identification

被引:25
作者
Li, Mingqiang [1 ]
Wang, Linjun [1 ,2 ]
Luo, Chengsheng [1 ]
Wu, Hongchun [1 ]
机构
[1] China Three Gorges Univ, Coll Mech & Power Engn, Hubei Key Lab Hydroelect Machinery Design & Mainte, Yichang 443002, Hubei, Peoples R China
[2] Queensland Univ Technol, Sch Chem Phys & Mech Engn, Brisbane, Qld 4001, Australia
关键词
Bridge; Moving load; Load identification; Identification accuracy; Improved fractional Tikhonov regularization; DYNAMIC LOAD IDENTIFICATION;
D O I
10.1016/j.istruc.2023.105840
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
To address the problem of poor recognition accuracy of integer -order Tikhonov regularization method (Tik) in identifying bridge moving loads, a bridge moving load recognition method based on Improved fractional Tikhonov (IF-Tik) regularization method is proposed in this paper. The bridge moving load identification model is established according to the theory of time domain method, and the process of driving a two -axle vehicle on the bridge is simulated. The moving load is represented as the differential form of the kernel function of bending moment response and acceleration response. The differential equation is transformed into a linear system of equations by the discretization method, and solved by the improved fractional -order Tikhonov regularization method. The results show that the IF-Tik method has certain advantages over the F-Tik and Tik methods in terms of recognition accuracy and noise immunity, and the IF-Tik method has a higher accuracy of load recognition, and the recognition error is only 15% of that of the F-Tik method; the recognition results are less affected by the moment response, and the proposed method has good robustness, and is more suitable for the bridge moving load identification.
引用
收藏
页数:11
相关论文
共 47 条
[1]   A novel algorithm for solving multiplicative mixed-norm regularization problems [J].
Aucejo, M. ;
De Smet, O. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2020, 144
[2]   A multiplicative regularization for force reconstruction [J].
Aucejo, M. ;
De Smet, O. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2017, 85 :730-745
[3]   The State of the Art of Data Science and Engineering in Structural Health Monitoring [J].
Bao, Yuequan ;
Chen, Zhicheng ;
Wei, Shiyin ;
Xu, Yang ;
Tang, Zhiyi ;
Li, Hui .
ENGINEERING, 2019, 5 (02) :234-242
[4]   Sparse l1 optimization-based identification approach for the distribution of moving heavy vehicle loads on cable-stayed bridges [J].
Bao, Yuequan ;
Li, Hui ;
Chen, Zhicheng ;
Zhang, Fujian ;
Guo, Anxin .
STRUCTURAL CONTROL & HEALTH MONITORING, 2016, 23 (01) :144-155
[5]  
Baumeister J, 1986, Vieweg
[6]   Regularization Methods Applied to Noisy Response from Beams under Static Loading [J].
Casero, Miguel ;
Covian, Enrique ;
Gonzalez, Arturo .
JOURNAL OF ENGINEERING MECHANICS, 2020, 146 (06)
[7]   An interpretive method for moving force identification [J].
Chan, THT ;
Law, SS ;
Yung, TH ;
Yuan, XR .
JOURNAL OF SOUND AND VIBRATION, 1999, 219 (03) :503-524
[8]  
Chang XT, 2014, Vibr Test Diagnos, V34, P6
[9]   Analysis of Factors Affecting the Accuracy of Moving Force Identification [J].
Chen, Zhen ;
Deng, Lu ;
Kong, Xuan .
INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS, 2021, 21 (02)
[10]   Identification of vehicle axle loads from bridge responses using preconditioned least square QR-factorization algorithm [J].
Chen, Zhen ;
Chan, Tommy H. T. ;
Nguyen, Andy ;
Yu, Ling .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2019, 128 :479-496