A computational method for the load spectra of large-scale structures with a data-driven learning algorithm  

作　　者：CHEN XianJia YUAN Zheng LI Qiang SUN ShouGuang WEI YuJie 

机构地区：[1]The State Key Laboratory of Nonlinear Mechanics,Institute of Mechanics,Chinese Academy of Sciences,Bejing 100190,China [2]School of Mechanical,Electronic and Control Engineering,Beijing Jiaotong University,Bejing 100044,China [3]School of Enginering Sciences,University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Science China(Technological Sciences)》2023年第1期141-154,共14页中国科学（技术科学英文版）

基　　金：supported by the Basic Science Center of the National Natural Science Foundation of China for “Multiscale Problems in Nonlinear Mechanics”(Grant No. 11988102);the National Key Research and Development Program of China (Grant Nos. 2017YFB0202800 and2016YFB1200602);the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB22020200);the Science Challenge Project (Grant No. TZ2018002)。

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 longterm 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.

关 键 词：load spectrum computational mechanics deep learning data-driven modeling gated recurrent unit neural network 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]