Modeling Asymmetric Hysteresis Inspired and Validated by Experimental Data

Asymmetric hysteresis is challenging to model: the classical Preisach model can be demanding in computation, while the Masing rules call for an adaptation from symmetric to asymmetric hysteresis. This study takes a small step by putting forth a method to capture asymmetric hysteretic restoring force as inspired and validated by a set of carefully designed and collected laboratory experimental data. The extended Masing model for symmetric hysteresis is generalized in several ways, for example, to express the restoring forces for loading/reloading and unloading branches as two different functions of the displacement and all displacements associated with the velocity turning points. The nonlinear parameter identification using the experimental data is carried out using multilayer feedforward neural networks.