Real-Time Hybrid Simulation Using Nonlinear Autoregressive with Exogenous Input Model through Data-Driven Arbitrary Polynomial Chaos Surrogate

Chen Meng-Hui; Chen Cheng; Gao Xiao-ShuGuo TongXu Wei-Jie

首页> 外文期刊>International journal of structural stability and dynamics >Real-Time Hybrid Simulation Using Nonlinear Autoregressive with Exogenous Input Model through Data-Driven Arbitrary Polynomial Chaos Surrogate

【24h】

Real-Time Hybrid Simulation Using Nonlinear Autoregressive with Exogenous Input Model through Data-Driven Arbitrary Polynomial Chaos Surrogate

机译：

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

The current practice of real-time hybrid simulation (RTHS) often requires specialized finite element programs for computational modeling of the analytical substructures. Considering the limited nonlinear modeling capacity or the increasing computation cost for complex modeling, surrogate models of the analytical substructure provide novel alternatives for RTHS to avoid finite element analysis with fast computation. This study explores the use of arbitrary polynomial chaos expansion (APC) and nonlinear autoregressive with exogenous input (NARX) model to emulate the dynamic behavior of analytical substructures in RTHS. The NARX model training can be conducted numerically in an off-line mode using existing general purpose finite element analysis software, and its implementation presents minimum computational demands on the RTHS equipment. RTHS of a single-degree-of-freedom structure with a self-centering viscous damper is conducted as proof of concept to experimentally demonstrate the effectiveness and superiority of the proposed APC-NARX-based approach. The APC is further compared with other metamodeling techniques including polynomial chaos expansion (PCE) and Kriging to surrogate NARX model coefficients to account for ground motion uncertainties in RTHS. It is demonstrated that APC-NARX modeling with optimal order enables better accuracy of RTHS results than those of Kriging- and PCE-NARX.

著录项

来源
《International journal of structural stability and dynamics》 |2023年第12期|共21页
作者
Chen Meng-Hui; Chen Cheng; Gao Xiao-ShuGuo TongXu Wei-Jie;
展开▼
作者单位

School of Civil Engineering, Southeast University, Nanjing 210096,P. R. China;

School of Engineering, San Francisco State University, San Francisco, 94132,USA;

School of Civil Engineering, Shandong University, Jinan 250061,P. R. China;

展开▼
收录信息
原文格式 PDF
正文语种英语
中图分类应用力学;
关键词
Real-time hybrid simulation; structural dynamics; nonlinear system identification; surrogate; uncertainty modeling; arbitrary polynomial chaos expansion;

相似文献

外文文献
中文文献

1. Data-driven nonlinear autoregressive with external input model-based compensation for real-time testing [J] . Weijie Xu, Cheng Chen, Xiaoshu GaoMenghui ChenTong GuoChangle Peng Structural control and health monitoring . 2022,第12期

机译：Data-driven nonlinear autoregressive with external input model-based compensation for real-time testing
2. Assessing uncertainty propagation in hybrid models for daily streamflow simulation based on arbitrary polynomial chaos expansion [J] . Zhou Pengxiao, Li Congcong, Li ZhongCai Yanpeng Advances in water resources . 2022,第2期

机译：Assessing uncertainty propagation in hybrid models for daily streamflow simulation based on arbitrary polynomial chaos expansion
3. Real-Time Hybrid Simulation with Polynomial Chaos NARX Modeling for Seismic Response Evaluation of Structures Subjected to Stochastic Ground Motions [J] . Xiaoshu Gao, Menghui Chen, Cheng ChenTong GuoWeijie XuKarlel Cornejo Journal of Structural Engineering . 2022,第9期

机译：Real-Time Hybrid Simulation with Polynomial Chaos NARX Modeling for Seismic Response Evaluation of Structures Subjected to Stochastic Ground Motions
4. Selection of relevant input variables in storm water quality modeling by multiobjective evolutionary polynomial regression paradigm [O] . Creaco E., Berardi, L., Sun, S. A., 2016

机译：selection of relevant input variables in storm water quality modeling by multiobjective evolutionary polynomial regression paradigm

Real-Time Hybrid Simulation Using Nonlinear Autoregressive with Exogenous Input Model through Data-Driven Arbitrary Polynomial Chaos Surrogate

摘要

著录项

相似文献

相关主题

期刊订阅