Adaptive Parametric Identification Scheme for a Class of Nondeteriorating and Deteriorating Nonlinear Hysteretic Behavior

Abstract

The adaptive parametric identification of deteriorating and nondeteriorating nonlinear hysteretic phenomena is considered using a generalization of Masing model based on the observed memory behavior of distributed element models. The model permits a parametric identification to be performed using nonlinear optimization techniques for arbitrary response time histories. A changing objective function, defined as the normalized force estimation error over a shifting window of recent data, is employed so that classic nonlinear optimization techniques can be used for the adaptive identification problem. A variation of the steepest descent method is used with significant modifications. To achieve the best performance for any given problem, a set of a priori numeric tests are suggested to design the identification scheme. The design identification scheme exhibits a very good performance in identifying the correct values of the parameters and is rather robust in dealing with noise. The proposed approach has applications to adaptive identification of much wider types of nonlinear rate-dependent hysteretic behavior. Also, the set of guidelines proposed by the authors is a contribution toward having more effective autonomous identification schemes, using minimal information about the model and input.