Moving force identification based on particle swarm optimization

Abstract

Moving force is very important for bridge design, structural analysis and structural health monitoring. Some studies on moving force identification (MFI) attract extensive attentions in the past decades. A novel two-step MFI method is proposed based on particle swarm optimization (PSO) and time domain method (TDM) in this study. The new proposed MFI method includes two steps. In the first step, the PSO is used to identify the constant loads without matrix inversion. In the second step, the conventional TDM is employed to estimate the rest time-varying loads where the Tikhonov regularization and general cross validation (GCV) are introduced to improve the MFI accuracy and to select optimal regularization parameters, respectively. A simply supported beam bridge subjected to moving forces is taken as a numerical simulation example to assess the performance of the proposed method. The illustrated results show that the new two-step MFI method can more effectively identify the moving forces compared to the conventional TDM and the improved Tikhonov regularization method, the proposed new method can provide more accurate MFI results on two moving forces under eight combinations of bridge responses.