Abstract:The ill-conditioned least squares problems often appear in structural damage recognition using noisy data to cause calculation results being fully distorted. Here, to significantly improve calculation accuracy, a feedback ridge estimate (FRE) technique was proposed to obtain accurate and stable damage recognition results. The proposed method has three steps. Firstly, the first ridge estimate (RE) calculation was done for linear equation set in structural damage evaluation to obtain the rough solution to damage parameters. Secondly, a new diagonal matrix was designed to be used in the second RE calculation according to the rough solution to damage parameters. Thirdly, the second RE calculation was done with FRE technique for linear equation set in structural damage evaluation to obtain damage parameters’ high-precision solution. A beam structure was taken as a numerical example to explore the effectiveness of the proposed method under 10% noise level. The calculation results were compared with those using the ordinary RE method and the singular value truncation (SVT) one. The results showed that the proposed FRE method can significantly improve calculation accuracy; even under 10% noise level, it can be used to obtain calculation results with very high precision.
杨秋伟,陆晨,罗帅,李翠红. 结构损伤识别的一种反馈岭估计方法[J]. 振动与冲击, 2020, 39(7): 43-50.
YANG Qiuwei, LU Chen, LUO Shuai, LI Cuihong. A feedback ridge estimate technique for structural damage recognition. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(7): 43-50.
J. J. Moughty, J. R. Casas. A state of the art review of modal-based damage detection in bridges: development, challenges, and solutions[J]. Applied Sciences, 2017, 7(5): 510.
A. H. H. Shahri, A. K. Ghorbani-Tanha. Damage detection via closed-form sensitivity matrix of modal kinetic energy change ratio[J]. Journal of Sound and Vibration, 2017, 401: 268-281.
A. Datteo, G. Quattromani, A. Cigada. On the use of AR models for SHM: A global sensitivity and uncertainty analysis framework[J]. Reliability Engineering & System Safety, 2018, 170: 99-115.
C. R. Ashokkumar, N. G. R. Lyengar. Partial eigenvalue assignment for structural damage mitigation. Journal of Sound and Vibration, 2011, 330: 9-16.
Chen XueFeng, Yang ZhiBo,Tian ShaoHua. A Review of the Damage Detection and Health Monitoring for Composite Structures[J]. Journal of Vibration,Measurement & Diagnosis,2018,1: 1-10.(in Chinese)
Q. W. Yang, J. K. Liu. Damage identification by the eigenparameter decomposition of structural flexibility change. International Journal for Numerical Methods in Engineering, 2009, 78(4): 444-459.
Zhan JiaWang, Yan YuZhi, Qiang WeiLiang. Damage Identification Method for Railway Pier Based on Frequency Response Function Similarity[J]. China Railway Science, 2018, 2: 37-43.(in Chinese)
W. J. Yan, W. X. Ren. A direct algebraic method to calculate the sensitivity of element modal strain energy. International Journal for Numerical Methods in Biomedical Engineering, 2011, 27(5): 694-710.
Xue JianYang, Bai FuYu, Zhang XiCheng. Seismic damage analysis and identification for the stiffness of ancient timber buildings mortise-tenon joints[J]. Journal of Vibration and Shock, 2018, 6: 47-54.(in Chinese)
Z. R. Lu, M. Huang, J. K. Liu. State-space formulation for simultaneous identification of both damage and input force from response sensitivity. Smart Structures and Systems, 2011, 8(2): 157-172.
C. N. Wong, H. Z. Huang, J. Q. Xiong, H. L. Lan. Generalized-order perturbation with explicit coefficient for damage detection of modular beam. Archive of Applied Mechanics, 2011, 81(4): 451-472.
A.K.Liu. A new class of some biased regression estimators[J]. CommStatist.Theory Methods, 1993.22.393-402.
KejianLiu. Using Liu-Type Estimator to Combat Collinearity[J]. Communications in Statistics, 2003, 32(5):1009-1020.
Liu YunHang, Song LiJie. Ridge Estimation Method for Ill-Conditioned Semiparametric Regression Model[J]. Hydrographic Surveying and Charting, 2008, 28(4):1-3.(in Chinese)
Wang LeYang, Xu CaiJun. Ridge Estimation Method in Inversion Problem with Ill-posed Constraint[J]. Geomatics and Information Science ofWuhan University, 2011, 36(5):612-616.(in Chinese)
Jibo Wu, Hu Yang. Efficiency of an almost unbiased two-parameter estimator in linear regression model[J]. Statistics, 2013, 47(3):535-545.
YalianLi, HuYang. On the Performance of the Jackknifed Modified Ridge Estimator in the Linear Regression Model with Correlated or Heteroscedastic Errors[J]. Communications in Statistics, 2011, 40(15):2695-2708.
Hansen P C. Truncated singular value decomposition solutions to discrete ill-posed problems with ill-determined numerical rank[M]. Society for Industrial and Applied Mathematics, 1990.
Xiong HongXia, Liu MuYu, Liu KeWen. Application of Wavelet Transform and SVD Method In Damage Monitoring of Structure[J]. Highway, 2009(3):96-101.(in Chinese)
Zhang LiTao, Li ZhaoXia, Fei QingGuo, et al. Studies On Some of Regularization Problems In Structural Dama[J]. Engineering Mechanics, 2008, 25(5):45-52.(in Chinese)
Guo HuiYong, Luo Le, Sheng Mao, et al. Structural damage identification method based on ridge estimation and L-curve[J]. Journal of Vibration & Shock, 2015, 34(4):200-204.(in Chinese)
Zhang Yong, Hou ZhiChao, Zhao YongLing. Finite element model updating based on response surface of the truncated singular values of frequency response functions[J]. Journal of Vibration Engineering, 2017, 30(3):341-348.(in Chinese)
He YunHan, Liu Kun. Damage Detection Method of Frame Structure Based on Singular Value Decomposition and Time Domain Sensitivity Analysis[J]. Open Journal of Acoustics and Vibration, 2018, 6(1):8-15(in Chinese)
Q. W. Yang. A new damage identification method based on structural flexibility disassembly. Journal of Vibration and Control, 2011, 17(7): 1000-1008.
Yang QiuWei, Sun Binxiang. An improved flexibility sensitivity method for structural damage detection[J]. Journal of Vibration and Shock, 2011,30(5):27-31. (in Chinese)
A. Neumaier. Solving ill-conditioned and singular linear systems: A tutorial on regularization[J]. SIAM review, 1998, 40(3): 636-666.
A. E. Hoerl, R. W. Kennard. Ridge regression: Biased estimation for nonorthogonal problems[J]. Techno metrics, 1970, 12(1): 55-67.
W. J. Hemmerle. An explicit solution for generalized ridge regression[J]. Techno metrics, 1975, 17(3): 309-314.
J. L. Mead, R. A. Renaut. A Newton root-finding algorithm for estimating the regularization parameter for solving ill-conditioned least squares problems[J]. Inverse Problems, 2008, 25(2): 025002.
T. C. Silva, A. A. Ribeiro, G. A. Periçaro. A new accelerated algorithm for ill-conditioned ridge regression problems[J]. Computational and Applied Mathematics, 2017: 1-18.
A. Gholami, H. M. Gheymasi. Regularization of geophysical ill-posed problems by iteratively re-weighted and refined least squares[J]. Computational Geosciences, 2016, 20(1): 19-33.
K. E. Prikopa, W. N. Gansterer, E. Wimmer. Parallel iterative refinement linear least squares solvers based on all-reduce operations[J]. Parallel Computing, 2016, 57: 167-184.
J. Lee, S. Cheon. Estimation for the multi-way error components model with ill-conditioned panel data[J]. Journal of the Korean Statistical Society, 2017, 46(1): 28-44.
L. W. Zhang, K. M. Liew. An improved moving least-squares Ritz method for two-dimensional elasticity problems[J]. Applied Mathematics and Computation, 2014, 246: 268-282.
X. Deng, L. Yin, S. Peng, et al. An iterative algorithm for solving ill-conditioned linear least squares problems[J]. Geodesy and Geodynamics, 2015, 6(6): 453-459.
H. Y. Jun, J. H. Park. Generation of optimal correlations by simulated annealing for ill-conditioned least-squares solution[J]. Journal of Nuclear Science and Technology, 2015, 52(5): 670-674.