Moving Force Identification Based on Group Lasso and Compressed Sensing

Abstract

Moving force identification (MFI) is one of the challenging tasks in bridge structural health monitoring (SHM). Especially when the vehicles get on and off the bridge, or cross bridge expansion joints and speed humps, the impact of vehicle loads on bridge structures is commonly focused and the accuracy of MFI results still needs further enhancements. To address this issue, a new MFI framework is proposed by using both group sparsity theory and compressed sensing (CS) in this study. Specifically, with the help of the relationship between moving vehicle loads and bridge responses induced by the traffic, a redundant dictionary based on CS is used to establish the motion equation of the vehicle–bridge system in conjunction with classical theory of MFI. A group structure on the sparse coefficient vector of each moving force tends to be divided into different groups (no overlapping), where sparse coefficient vectors and measurement matrices are divided in different groupings and eventually reconstructed for unknown vectors of moving forces. Additionally, the effect of different ratios of number of groups to sparsity (g/k) on the MFI results is considered. Finally, numerical simulations and experimental verifications are carried out to assess the performance and capability of the proposed framework. The illustrated results show that the group lasso could precisely reconstruct the moving forces more accurately after proper grouping. The proposed new MFI framework outperforms the traditional L2regularization or CS methods with a higher identification accuracy and a good robustness to measurement noises, which can be effectively used for the MFI problem in practice.