Abstract
Superelastic shape memory alloy (SMA) wires exhibit superb hysteretic energy dissipation and deformation capabilities. Therefore, they are increasingly used for the vibration control of civil engineering structures. The efficient design of SMA-based control devices requires accurate material models. However, the thermodynamically coupled SMA behavior is highly sensitive to strain rate. For an accurate modelling of the material behavior, a wide range of parameters needs to be determined by experiments, where the identification of thermodynamic parameters is particularly challenging due to required technical instruments and expert knowledge. For an efficient identification of thermodynamic parameters, this study proposes a machine-learning-based approach, which was specifically designed considering the dynamic SMA behavior. For this purpose, a feedforward artificial neural network (ANN) architecture was developed. For the generation of training data, a macroscopic constitutive SMA model was adapted considering strain rate effects. After training, the ANN can identify the searched model parameters from cyclic tensile stress–strain tests. The proposed approach is applied on superelastic SMA wires and validated by experiments.
Highlights
Lehrstuhl für Baustatik und Baudynamik, Department of Civil Engineering, RWTH Aachen University, Abstract: Superelastic shape memory alloy (SMA) wires exhibit superb hysteretic energy dissipation and deformation capabilities
This paper proposes for dynamic applications of superelastic SMA wires an artificial neural network (ANN)-based parameter identification (PI) methodology, which considers the strain rate dependency of the material and focuses on the identification of thermodynamic parameters from stress–strain responses
High mechanical stresses induce a forward-phase transformation (AM), and the SMA crystals reorient their atomic grid from body centered (B2) to a monoclinic (B19) lattice, which is more stable for high stress levels
Summary
Lehrstuhl für Baustatik und Baudynamik, Department of Civil Engineering, RWTH Aachen University, Abstract: Superelastic shape memory alloy (SMA) wires exhibit superb hysteretic energy dissipation and deformation capabilities. They are increasingly used for the vibration control of civil engineering structures. The efficient design of SMA-based control devices requires accurate material models. For an efficient identification of thermodynamic parameters, this study proposes a machine-learning-based approach, which was designed considering the dynamic SMA behavior. For this purpose, a feedforward artificial neural network (ANN) architecture was developed. Brinson [9] and Auricchio and
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