A novel computational inverse technique for load identification using the shape function method of moving least square fitting

• An inverse method is proposed to identify dynamic load in practical problems.
• The local load is approximated by shape functions under least square fitting.
• The whole response matrix is formed by assembling the responses of the functions.
• The dynamic load can be approximated more continuously and smoothly.
• A regularization method is adopted to overcome the ill-posedness.

Based on shape function method of moving least square fitting (SFM_MLSF), dynamic load is identified. The time domain of load is discretized and the local load is approximated by SFM under LSF. With this local domain moving, whole load is described. The response matrix is formed through assembling the responses of shape function loads in all local domains and the forward model is established. The regularization is adopted to overcome the ill-posedness of load reconstruction. Compared with Green’s kernel function method (GKFM), SFM_MLSF approximates load more smoothly, so the ill-posedness is improved. Numerical simulations demonstrate the efficiency of SFM_MLSF.