Lenzen Niklas, Altay Okyay
Lehrstuhl für Baustatik und Baudynamik, Department of Civil Engineering, RWTH Aachen University, 52074 Aachen, Germany.
Materials (Basel). 2022 Jan 1;15(1):304. doi: 10.3390/ma15010304.
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.
超弹性形状记忆合金（SMA）丝具有出色的滞后能量耗散和变形能力。因此，它们越来越多地用于土木工程结构的振动控制。基于SMA的控制装置的高效设计需要精确的材料模型。然而，热力学耦合的SMA行为对应变速率高度敏感。为了准确模拟材料行为，需要通过实验确定广泛的参数，其中由于所需的技术仪器和专业知识，热力学参数的识别特别具有挑战性。为了高效识别热力学参数，本研究提出了一种基于机器学习的方法，该方法是专门考虑动态SMA行为而设计的。为此，开发了一种前馈人工神经网络（ANN）架构。为了生成训练数据，考虑应变速率效应调整了宏观本构SMA模型。训练后，ANN可以从循环拉伸应力-应变试验中识别搜索到的模型参数。所提出的方法应用于超弹性SMA丝并通过实验进行了验证。
