Structural load identification and response reconstruction based on joint MT-JBDQR regularization

YIN Hong; WANG Sheng; PENG Zhenrui

doi:10.11860/j.issn.1673-0291.20250058

Abstract：To address the ill-posedness of transfer matrices and the poor noise robustness encountered in load identification and response reconstruction,this study proposes a joint regularization method that integrates Modified Truncated Randomized Singular Value Decomposition(MTRSVD)with Joint Bidiagonalization QR(JBDQR).By alleviating matrix ill-posedness and reducing the impact of mea-surement noise on identification results,the method can identify unknown loads and reconstruct re-sponses at unmeasured locations based solely on limited measurement information.First,the struc-tural dynamic equations are derived,and a state-space model and transfer matrix are established to for-mulate the load identification and response reconstruction problem.Then,MTRSVD is used to pre-process the transfer matrix,and a randomized projection technique is used to reduce matrix dimension-ality,while an adaptive truncation criterion preserves the dominant features,thereby alleviating the ill-posedness of the transfer matrix and reducing the influence of measurement noise.Subsequently,the JBDQR algorithm is introduced for load identification,in which unknown loads are solved via iterative regularization through a joint bidiagonalization process,and responses at unmeasured locations are re-constructed using the corresponding transfer matrices.Finally,the proposed method is validated through the numerical example of a 3 kW small wind turbine blade and experimental tests on a simply supported beam.The results indicate that the proposed method can still effectively achieve load identi-fication and the response reconstruction at unmeasured locations under a noise level of 15%.