Data-Based Nonlinear Identification and Constitutive Modeling of Hysteresis in NiTiNOL and Steel Strands

Several different data-driven strategies for nonlinear identification are applied to experimental data exhibiting various types of hysteretic behavior. The experimental data contain displacement and restoring force information for several tests conducted using different configurations of a rheological testing device with various assemblies of nickel titanium-Naval Ordnance Laboratory (NiTiNOL) and steel wire strands. Among the different configurations, the response of the wire strands shows three distinct forms of nonlinear behavior: classical quasi-linear softening hysteresis; strongly pinched, hardening hysteresis; and slightly pinched, hardening hysteresis. The data-driven methods applied for nonlinear identification include polynomial basis functions and neural networks. The polynomial basis nonlinear identification methods are used for the construction and characterization of reduced-order models to gain insight into the physical modeling of the hysteretic phenomena. The neural network methods are found to be more useful for predictive purposes, demonstrating an ability to produce accurate results on both training and testing data.