Research on Dynamic Load Identification Based on the Newmark-β Method and Novel Iterative Regularization Strategy

Abstract

This work focuses on the dynamic load identification motivated by Newmark-β Method and regularization strategy.  Given the ill-posed nature of load identification, an improved hybrid LSQR regularization strategy is proposed, which incorporates an adaptive correction mechanism by iteratively comparing the computed response with the measured response. For load identification problems with prior conditions, an augmented hybrid LSQR regularization strategy is further developed, which expands the Krylov subspace with prior information and combines the improved hybrid LSQR regularization strategies to ensure stable iteration. Simulation examples using a simply supported beam as a representative structure of continuous systems, involving different load types and noise levels, validate the stability and accuracy of the two methods. The results show that the improved hybrid LSQR algorithm exhibits superior stability and accuracy compared to the conventional LSQR algorithm and yields relatively favorable identification results. Moreover, the superposition load identification simulation indicates that the augmented hybrid LSQR algorithm further improves the accuracy of load identification when prior knowledge of the load is available.