Identification of nonlinear structural parameters based on particle filtering algorithm - Dandan Xia; Wanghua Yu; Li Lin (Results in Engineering)
Structures such as large-span flexible bridges and high-rise buildings exhibit significant nonlinearity due to their flexibility. Therefore, a nonlinear identification method is essential for structural parameter identification in these cases. Based on the strong nonlinear Bouc-Wen model, the particle filtering (PF) method is employed to identify the dynamic response and structural parameters of nonlinear structures under seismic loading. To show the performance of parameters identification, the PF method is compared with Unscented Kalman Filter method as well. The results show that the particle filtering method effectively identifies the strong nonlinear model with multiple degrees of freedom with partial measurements. As the number of particles increases, the identification error decreases; however, this also leads to increase of simulation time. Considering both the accuracy of identification results and the efficiency of the process, the best performance is achieved when N = 150 in this simulation. Based on response data from verified numerical simulation by computational fluid dynamics (CFD) method, results show that the PF method can be applied on fluid-structure vibration systems. This study provides a reference for the application of particle filtering in the health monitoring of large nonlinear structures.