DATA DRIVEN IDENTIFICATION OF NONLINEAR STRUCTURAL SYSTEMS WITH PARTIAL RESPONSE MEASUREMENTS

As well known, nonlinearity widely exists in structures under severe excitation. Thus, the research on the identification of structural nonlinearity plays a very important role in the structural safety assessment and damage diagnosis. In this paper, with partial measurements of structural responses, a data driven algorithm is proposed for the identification of nonlinear structural systems without the priori knowledge of the nature and mathematical form of the nonlinearities. This algorithm consists of two stages: In the first stage, the identification of an equivalent linear system of a nonlinear structural system is conducted based on the extended Kalman filter, and the locations of nonlinearity are identified by comparing the difference between the structural parameters of linear system and those of equivalent linear system. Then, the nonlinear restoring force is expanded by power series polynomial, and the coefficients of the series are identified by the extended Kalman filter. The feasibility and robustness of the proposed algorithm are verified a numerical simulation example.