STRUCTURAL NONLINEAR DAMAGE IDENTIFICATION BASED ON THE GNAR MODEL AND ITAKURA DISTANCE

To solve the problem of time-domain nonlinear damage identification, a damage identification method based on the general expression of the linear and nonlinear auto-regressive model (GNAR model) and the Itakura distance is proposed. Describe the basic theory and give the basic model order determination and parameter estimation methods. The optimized structure of the GNAR model is selected by the pruning algorithm. The Itakura distance is used as the damage indicator for nonlinear damage identification. The nonlinear damage experiment of a three-story frame was used to verify the effectiveness of the proposed method. The method is applied to the nonlinear damage identification experiment of a transmission tower steel frame model. The results show that the proposed nonlinear damage identification method can easily identify the nonlinear damage of the frame and transmission tower steel frame structure. The environmental change has little effect on the damage identification results. The damage probability of the damaged floor calculated by the proposed method is higher than the undamaged floors. The proposed method is helpful to find the location of the nonlinear damage source efficiently.