论文标题
使用遗传算法将环状实验载荷数据拟合载荷数据拟合到枢轴磁滞模型
Fitting Cyclic Experimental Load-Deformation Data to The Pivot Hysteresis Model Using Genetic Algorithm
论文作者
论文摘要
了解结构材料或结构框架中负载与变形之间的线性或非线性关系是进行适当且代表性良好的模拟的关键。这项研究致力于建模从实验结果中捕获的循环载荷变形滞后关系，并利用它来表示环状磁滞数据。遗传算法用于找到最佳参数，以引入模型并最大程度地减少模拟和实验结果之间的偏差。换句话说，发现与任何位移模式的加载响应相关的参数，同时最大程度地减少了模拟加载响应与从实验中执行的加载数据之间的偏差。首先，为了减小测量设备或线性变量差分变压器（简称LVDT）记录的数据大小，提出了数据重采样技术。其次，重采样数据用于遗传算法中，以寻找描述模型的最佳参数。该方法可用于训练模型，以预测材料和帧的能力和性能，当它们暴露于任何变形模式时。本文的末尾显示了许多例子。
Understanding the linear or nonlinear relationship between load and deformation in structural materials or structural frames is a key to a proper and a well-represented simulation. This research is dedicated to model a cyclic load-deformation hysteresis relationship, captured from experimental results, and utilize it to represent the cyclic hysteresis data. The Genetic Algorithm is used to find the best parameters to introduce the model and to minimize the deviation between the simulation and the experimental results. In other words, the parameters associated with the loading response of any displacement pattern, are found, while minimizing the deviation between simulation loading response and the loading data carried out from the experiment. First, to reduce the data size recorded with measuring devices or Linear Variable Differential Transformers (LVDTs in short), a data resampling technic is proposed. Second, the resampled data is used in Genetic Algorithm to seek for the best parameters to describe the model. This method could be used to train models to predict the capacity and performance of a materials and frames when they are exposed to any deformation patterns. Numerous examples are shown at the end of the paper.
