A boosted deep learning-based approach for near real-time response estimation of structures under ground motion excitations

IF 2.6 3区 工程技术 Q2 ENGINEERING, CIVIL Structure and Infrastructure Engineering Pub Date : 2024-09-17 DOI:10.1080/15732479.2024.2396613
Mohammad Javad Kaffashchian, Mojtaba Salkhordeh, Reza Karami Mohammadi
{"title":"A boosted deep learning-based approach for near real-time response estimation of structures under ground motion excitations","authors":"Mohammad Javad Kaffashchian, Mojtaba Salkhordeh, Reza Karami Mohammadi","doi":"10.1080/15732479.2024.2396613","DOIUrl":null,"url":null,"abstract":"This article presents an improved Convolutional Neural Network (CNN)-based approach to predict the vibration response of structures under seismic events without installing sensors at different stor...","PeriodicalId":49468,"journal":{"name":"Structure and Infrastructure Engineering","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structure and Infrastructure Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/15732479.2024.2396613","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: 0

Abstract

This article presents an improved Convolutional Neural Network (CNN)-based approach to predict the vibration response of structures under seismic events without installing sensors at different stor...
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
基于增强型深度学习的方法,用于对地动激励下的结构进行近实时响应估算
本文介绍了一种基于卷积神经网络(CNN)的改进方法,用于预测地震事件下结构的振动响应,而无需在不同位置安装传感器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Structure and Infrastructure Engineering
Structure and Infrastructure Engineering 工程技术-工程:机械
CiteScore
9.50
自引率
8.10%
发文量
131
审稿时长
5.3 months
期刊介绍: Structure and Infrastructure Engineering - Maintenance, Management, Life-Cycle Design and Performance is an international Journal dedicated to recent advances in maintenance, management and life-cycle performance of a wide range of infrastructures, such as: buildings, bridges, dams, railways, underground constructions, offshore platforms, pipelines, naval vessels, ocean structures, nuclear power plants, airplanes and other types of structures including aerospace and automotive structures. The Journal presents research and developments on the most advanced technologies for analyzing, predicting and optimizing infrastructure performance. The main gaps to be filled are those between researchers and practitioners in maintenance, management and life-cycle performance of infrastructure systems, and those between professionals working on different types of infrastructures. To this end, the journal will provide a forum for a broad blend of scientific, technical and practical papers. The journal is endorsed by the International Association for Life-Cycle Civil Engineering ( IALCCE) and the International Association for Bridge Maintenance and Safety ( IABMAS).
期刊最新文献
Vibration based dual-criteria damage detection method using deep neural networks in highway bridges with steel girders Numerical study on flexural behaviour of sulphate corroded RC beams strengthened with ultra-high-performance concrete A boosted deep learning-based approach for near real-time response estimation of structures under ground motion excitations Structural frequency-based maintenance management method for steel truss bridges under atmospheric corrosion Investigation of the time-dependent bearing capacities of concrete structures in different environments considering climate change
×
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