用LSTM预测输电塔线系统风致结构响应

IF 2.1 3区 工程技术 Q2 ENGINEERING, CIVIL Smart Structures and Systems Pub Date : 2021-09-01 DOI:10.12989/SSS.2021.28.3.391
Jiayue Xue, Ge Ou
{"title":"用LSTM预测输电塔线系统风致结构响应","authors":"Jiayue Xue, Ge Ou","doi":"10.12989/SSS.2021.28.3.391","DOIUrl":null,"url":null,"abstract":"Wind-induced dynamic response of the nonlinear structure is critical for the structural safety and reliability. The traditional approaches for this response including observation or simulation focus on the structural health monitoring, the experiment, or finite element model development. However, all these approaches require high cost or computational investment. This paper proposes to predict the wind-induced dynamic response of the nonlinear structure with a novel deep learning approach, LSTM, and applies this in a structural lifeline system, the transmission tower-line system. By constructing the optimized LSTM architectures, the proposed method applies to both the linear structure, the single transmission tower and the nonlinear structure, the transmission tower-line system, with promising results for the dynamic and extreme response prediction. It can conclude that the layers and the hidden units have a strong impact on the LSTM prediction performance, and with proper training data set, the computational time can significantly decrease. A comparison surrogate model developed by CNN is also utilized to demonstrate the robustness of the LSTM-based surrogate model with limited data scale.","PeriodicalId":51155,"journal":{"name":"Smart Structures and Systems","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Predicting wind-induced structural response with LSTM in transmission tower-line system\",\"authors\":\"Jiayue Xue, Ge Ou\",\"doi\":\"10.12989/SSS.2021.28.3.391\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Wind-induced dynamic response of the nonlinear structure is critical for the structural safety and reliability. The traditional approaches for this response including observation or simulation focus on the structural health monitoring, the experiment, or finite element model development. However, all these approaches require high cost or computational investment. This paper proposes to predict the wind-induced dynamic response of the nonlinear structure with a novel deep learning approach, LSTM, and applies this in a structural lifeline system, the transmission tower-line system. By constructing the optimized LSTM architectures, the proposed method applies to both the linear structure, the single transmission tower and the nonlinear structure, the transmission tower-line system, with promising results for the dynamic and extreme response prediction. It can conclude that the layers and the hidden units have a strong impact on the LSTM prediction performance, and with proper training data set, the computational time can significantly decrease. A comparison surrogate model developed by CNN is also utilized to demonstrate the robustness of the LSTM-based surrogate model with limited data scale.\",\"PeriodicalId\":51155,\"journal\":{\"name\":\"Smart Structures and Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2021-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Smart Structures and Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.12989/SSS.2021.28.3.391\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Structures and Systems","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.12989/SSS.2021.28.3.391","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 5

摘要

非线性结构的风动力响应对结构的安全性和可靠性至关重要。这种反应的传统方法,包括观察或模拟,侧重于结构健康监测、实验或有限元模型开发。然而,所有这些方法都需要高成本或计算投资。本文提出了一种新的深度学习方法LSTM来预测非线性结构的风致动力响应,并将其应用于结构生命线系统——输电塔-线路系统。通过构建优化的LSTM体系结构,该方法适用于线性结构、单塔输电和非线性结构、输电塔-线路系统,在动态和极端响应预测方面取得了良好的效果。可以得出结论,层和隐藏单元对LSTM预测性能有很大影响,并且通过适当的训练数据集,可以显著减少计算时间。CNN开发的比较代理模型也用于证明基于LSTM的代理模型在有限数据规模下的稳健性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Predicting wind-induced structural response with LSTM in transmission tower-line system
Wind-induced dynamic response of the nonlinear structure is critical for the structural safety and reliability. The traditional approaches for this response including observation or simulation focus on the structural health monitoring, the experiment, or finite element model development. However, all these approaches require high cost or computational investment. This paper proposes to predict the wind-induced dynamic response of the nonlinear structure with a novel deep learning approach, LSTM, and applies this in a structural lifeline system, the transmission tower-line system. By constructing the optimized LSTM architectures, the proposed method applies to both the linear structure, the single transmission tower and the nonlinear structure, the transmission tower-line system, with promising results for the dynamic and extreme response prediction. It can conclude that the layers and the hidden units have a strong impact on the LSTM prediction performance, and with proper training data set, the computational time can significantly decrease. A comparison surrogate model developed by CNN is also utilized to demonstrate the robustness of the LSTM-based surrogate model with limited data scale.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Smart Structures and Systems
Smart Structures and Systems 工程技术-工程:机械
CiteScore
6.50
自引率
8.60%
发文量
0
审稿时长
9 months
期刊介绍: An International Journal of Mechatronics, Sensors, Monitoring, Control, Diagnosis, and Management airns at providing a major publication channel for researchers in the general area of smart structures and systems. Typical subjects considered by the journal include: Sensors/Actuators(Materials/devices/ informatics/networking) Structural Health Monitoring and Control Diagnosis/Prognosis Life Cycle Engineering(planning/design/ maintenance/renewal) and related areas.
期刊最新文献
Analysis, optimization and control of an adaptive tuned vibration absorber featuring magnetoactive materials Numerical investigation on cyclic behaviour of superelastic shape memory alloy (SMA) dampers Hybrid fragility curve derivation of buildings based on post-earthquake reconnaissance data A corrosion threshold-controllable sensing system of Fe-C coated long period fiber gratings for life-cycle mass loss measurement of steel bars with strain and temperature compensation Steel dual-ring dampers: Micro-finite element modelling and validation of cyclic behavior
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1