Optimization analysis and experimental study of preconditioned least square QR-factorization for moving force identification

被引:0
|
作者
Chen Z. [1 ,2 ]
Wang Z. [1 ]
Yu L. [2 ]
Shao W.-D. [1 ]
机构
[1] School of Civil Engineering and Communication, North China University of Water Resources and Electric Power, Zhengzhou
[2] Key Laboratory of Disaster Forecast and Control in Engineering of Ministry of Education, Jinan University, Guangzhou
关键词
Bridge; Moving force identification; Optimization analysis; Preconditioned least square QR-factorization; Time domain method;
D O I
10.16385/j.cnki.issn.1004-4523.2018.04.001
中图分类号
学科分类号
摘要
Based on the theory of moving force identification in time domain, a preconditioned least square QR-factorization (PLSQR) algorithm is developed to overcome the typical ill-posed problem existing in inverse problems. A comprehensive numerical simulation is set up based on a beam model with biaxial time-varying forces to evaluate PLSQR by comparing this technique with the conventional counterpart SVD embedded in the time domain method (TDM). Investigations show that the PLSQR has higher precision, more noise immunity and less sensitive to perturbations with the ill-posed problems compared with TDM. By combining the improved Gram-Schmidt with iterative orthogonalization, iterative optimization analysis of PLSQR is carried out. Results indicate that the improved PLSQR(i-PLSQR) can more quickly and effectively identify the moving load on bridge without sacrificing the identification accuracy compared with the PLSQR, and the average optimal numbers of iterations reduce by at least two thirds in eight cases with three noise levels. The experimental results show that the identified force obtained from the i-PLSQR is very close to the true force and the identification accuracy is significantly higher than traditional TDM, which can be applied to the field moving force identification. The study results have important reference for the research of inverse problems identification of structural dynamics. © 2018, Nanjing Univ. of Aeronautics an Astronautics. All right reserved.
引用
收藏
页码:545 / 552
页数:7
相关论文
共 17 条
  • [11] Xu X., Ou J.P., Force identification of dynamic systems using virtual work principle, Journal of Sound and Vibration, 337, pp. 71-94, (2015)
  • [12] Qiao B.J., Zhang X.W., Luo X.J., Et al., A force identification method using cubic B-spline scaling functions, Journal of Sound and Vibration, 337, pp. 28-44, (2015)
  • [13] Chen Z.C., Li H., Bao Y.Q., Identification of spatio-temporal distribution of vehicle loads on long-span bridges using computer vision technology, Structural Control and Health Monitoring, 23, 3, pp. 517-534, (2016)
  • [14] Paige C.C., Saunders M.A., LSQR: An algorithm for sparse linear equations and sparse least squares, ACM Transactions on Mathematical Software, 8, pp. 43-71, (1982)
  • [15] Ning S.-W., Shi Z.-Y., QR decomposition-based least squares lattice adaptive filter algorithms for active noise control, Journal of Vibration Engineering, 26, 3, pp. 363-373, (2013)
  • [16] Jacobsen M., Hansen P.C., Saunders M.A., Subspace preconditioned LSQR for discrete ill-posed problems, BIT Numerical Mathematics, 43, pp. 975-989, (2003)
  • [17] Dax A., A modified Gram-Schmidt algorithm with iterative orthogonalization and column pivoting, Linear Algebra and its Applications, 310, pp. 25-42, (2000)