Data-Driven Substructuring Technique for Pseudo-Dynamic Hybrid Simulation of Steel Braced Frames
Data-Driven Substructuring Technique for Pseudo-Dynamic Hybrid Simulation of Steel Braced Frames
复制标题
钢支撑框架伪动力混合仿真的数据驱动子结构技术
DOI：
10.1007/978-3-031-03811-2_42
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
A. Imanpour
中科院分区：
文献类型：
--
作者：
F. Mokhtari;A. Imanpour
.本文提出了一种新的子结构技术，用于混合模拟钢支撑框架结构在地震荷载下，其中一个新的机器学习为基础的模型来预测钢支撑的滞回响应。证实的数值数据用于训练模型，称为PI-SINDy，开发与普朗特-Ishlinskii滞后模型和稀疏识别算法的帮助。通过用训练好的PI-SINDy模型代替原型钢结构屈曲约束支撑框架的支撑部分，建立了一种新的仿真技术--数据驱动混合仿真（DDHS）。通过对地震作用下原型框架的非线性反应时程分析，评价了DDHS的精度。与基准纯数值模型的对比结果表明，所提出的模型可以准确地预测钢屈曲约束支撑的滞回响应。
. This paper proposes a new substructuring technique for hybrid simulation of steel braced frame structures under seismic loading in which a new machine learning-based model is used to predict the hysteretic response of steel braces. Corroborating numerical data is used to train the model, referred to as PI-SINDy, developed with the aid of the Prandtl-Ishlinskii hysteresis model and sparse identification algorithm. By replacing a brace part of a prototype steel buckling-restrained braced frame with the trained PI-SINDy model, a new simulation technique referred to as data-driven hybrid simulation (DDHS) is established. The accuracy of DDHS is evaluated using the nonlinear response history analysis of the prototype frame subjected to an earthquake ground motion. Compared to a baseline pure numerical model, the results show that the proposed model can accurately predict the hysteretic response of steel buckling-restrained braces.