中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 力学研究所
  3. 中国科学院力学研究所
  4. 非线性力学国家重点实验室
A computational method for the load spectra of large-scale structures with a data-driven learning algorithm

文献类型：期刊论文

作者Chen, XianJia3; Yuan, Zheng2; Li, Qiang2; Sun, ShouGuang2; Wei, YuJie1,3; Wei YJ(魏宇杰); Chen XJ(陈贤佳); Wei YJ(魏宇杰); Chen XJ(陈贤佳)
刊名SCIENCE CHINA-TECHNOLOGICAL SCIENCES
出版日期2022-12-26
页码14
关键词load spectrum computational mechanics deep learning data-driven modeling gated recurrent unit neural network
ISSN号1674-7321
DOI10.1007/s11431-021-2068-8
通讯作者Wei, YuJie(yujie_wei@lnm.imech.ac.cn)
英文摘要For complex engineering systems, such as trains, planes, and offshore oil platforms, load spectra are cornerstone of their safety designs and fault diagnoses. We demonstrate in this study that well-orchestrated machine learning modeling, in combination with limited experimental data, can effectively reproduce the high-fidelity, history-dependent load spectra in critical sites of complex engineering systems, such as high-speed trains. To meet the need for in-service monitoring, we propose a segmentation and randomization strategy for long-duration historical data processing to improve the accuracy of our data-driven model for long-term load-time history prediction. Results showed the existence of an optimal length of subsequence, which is associated with the characteristic dissipation time of the dynamic system. Moreover, the data-driven model exhibits an excellent generalization capability to accurately predict the load spectra for different levels of passenger-dedicated lines. In brief, we pave the way, from data preprocessing, hyperparameter selection, to learning strategy, on how to capture the nonlinear responses of such a dynamic system, which may then provide a unifying framework that could enable the synergy of computation and in-field experiments to save orders of magnitude of expenses for the load spectrum monitoring of complex engineering structures in service and prevent catastrophic fatigue and fracture in those solids.
WOS关键词NEURAL-NETWORKS ; DEEP ; ESTABLISHMENT ; PREDICTION
资助项目Basic Science Center of the National Natural Science Foundation of China for Multiscale Problems in Non-linear Mechanics[11988102] ; National Key Research and Development Program of China[2017YFB0202800] ; National Key Research and Development Program of China[2016YFB1200602] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB22020200] ; Science Challenge Project[TZ2018002]
WOS研究方向Engineering ; Materials Science
语种英语
WOS记录号WOS:000907092900007
资助机构Basic Science Center of the National Natural Science Foundation of China for Multiscale Problems in Non-linear Mechanics ; National Key Research and Development Program of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Science Challenge Project
源URL[http://dspace.imech.ac.cn/handle/311007/91436]  
专题力学研究所_非线性力学国家重点实验室
通讯作者Wei, YuJie; Wei YJ(魏宇杰)
作者单位1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
2.Beijing Jiaotong Univ, Sch Mech Elect & Control Engn, Beijing 100044, Peoples R China
3.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Chen, XianJia,Yuan, Zheng,Li, Qiang,et al. A computational method for the load spectra of large-scale structures with a data-driven learning algorithm[J]. SCIENCE CHINA-TECHNOLOGICAL SCIENCES,2022:14.
APA Chen, XianJia.,Yuan, Zheng.,Li, Qiang.,Sun, ShouGuang.,Wei, YuJie.,...&Chen XJ.(2022).A computational method for the load spectra of large-scale structures with a data-driven learning algorithm.SCIENCE CHINA-TECHNOLOGICAL SCIENCES,14.
MLA Chen, XianJia,et al."A computational method for the load spectra of large-scale structures with a data-driven learning algorithm".SCIENCE CHINA-TECHNOLOGICAL SCIENCES (2022):14.

入库方式： OAI收割

来源：力学研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。