Identification of Nonlinear Structural Parameters Using Combined Power Flow and Acceleration Matching Approaches

Abstract

Identification of structures with nonlinearity in stiffness and damping is a challenging research problem especially in the area of Structural Health Monitoring. To develop an accurate mathematical model, it is essential to consider the nonlinearities associated with the system and subsequently identify the parameters in the model. The system characterization is an important phase in testing which provides prior knowledge about the nonlinear behavior of the system. Once the system is well characterized, any of the system identification techniques can be used to identify the model parameters or nonlinear coefficients with varying degree of accuracy. Identification uses information from both the characterization process and input-output experiments to estimate the nonlinear system parameters associated with the system. The present paper mainly focuses on the procedural technique to identify the nonlinear parameters of structures using collective information of both substructure and novel application of power flows in time domain, using Particle Swarm Optimization.