A Novel Regularized Adaptive Matching Pursuit for Moving Force Identification Using Multiple Criteria and Prior Knowledge

Abstract

Moving force identification (MFI) is one of the challenging tasks in structural health monitoring (SHM) of bridges. As an inverse problem, continuous attention is needed to address the ill-posedness of MFI system matrix, computational efficiency and accuracy. Therefore, a novel regularized adaptive matching pursuit (NRAMP) framework is proposed for MFI using multiple criteria and prior knowledge in this study. Firstly, a relationship between moving forces and structural responses is established. With the utilization of redundant matrix, the MFI problem is converted into one of the sparse recoveries. A new adaptive criterion related to atoms both in the sparse regularization and LSQR factorization is introduced into the regularized orthogonal matching pursuit (ROMP) process. The ill-posedness of system matrix in sparse recovery can be reduced greatly, and the unknown sparsity problem can be skipped. Furthermore, the optimal atoms of redundant matrix will be selected repeatedly based on another criterion related to prior knowledge that the static axle-weight of a vehicle is the main component of moving vehicle force. The residual in each iteration will be saved and the atoms with the smallest residual are chosen at last. Finally, to assess the feasibility of the proposed method, numerical simulations on identification of single moving force with impulse components and two unequal moving forces, and experimental verifications on MFI of a model vehicle moving on a beam in laboratory are also carried out. The results show that the relative percentage errors between the identified and true gross vehicle weight keep under 3.6% in all measured cases, and the executive time of the proposed method is far less than that due to common OMP methods.