A computational method for the load spectra of large-scale structures with a data-driven learning algorithm | |
Chen, XianJia3; Yuan, Zheng2; Li, Qiang2; Sun, ShouGuang2; Wei, YuJie1,3 | |
刊名 | SCIENCE CHINA-TECHNOLOGICAL SCIENCES |
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 |
DOI | 10.1007/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. |
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关键词 | NEURAL-NETWORKS ; DEEP ; ESTABLISHMENT ; PREDICTION |
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 |
内容类型 | 期刊论文 |
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 |
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.(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. |
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