通过对原始阻抗信号进行一维 CNN 深度学习,实现基于智能集料的混凝土应力监测

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Structural Control & Health Monitoring Pub Date : 2024-03-09 DOI:10.1155/2024/5822653
Quoc-Bao Ta, Quang-Quang Pham, Ngoc-Lan Pham, Thanh-Canh Huynh, Jeong-Tae Kim
{"title":"通过对原始阻抗信号进行一维 CNN 深度学习,实现基于智能集料的混凝土应力监测","authors":"Quoc-Bao Ta,&nbsp;Quang-Quang Pham,&nbsp;Ngoc-Lan Pham,&nbsp;Thanh-Canh Huynh,&nbsp;Jeong-Tae Kim","doi":"10.1155/2024/5822653","DOIUrl":null,"url":null,"abstract":"<div>\n <p>A 1-dimensional convolutional neural network (1D CNN) model is developed to process deep learning of raw impedance signals for smart aggregate (SA)-based concrete stress monitoring. First, the framework of the SA-based stress monitoring using deep learning of raw impedance signals is described. An impedance measurement model is designed for a SA-embedded concrete body under compression. A 1D CNN model is developed for deep learning of raw impedance signals corresponding to various stress levels. Three approaches for concrete stress monitoring are designed to deal with data availability, signal noises, and untrained stress levels. Second, a few SA-embedded concrete cylinders are experimented to measure impedance signals under various stress levels. Finally, the performance of the proposed method is extensively evaluated by investigating the feasibility of the K-fold cross-validation to deal with the data availability and the effects of signal noises and untrained data on the accuracy of stress estimation in the SA-embedded concrete cylinders.</p>\n </div>","PeriodicalId":49471,"journal":{"name":"Structural Control & Health Monitoring","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5822653","citationCount":"0","resultStr":"{\"title\":\"Smart Aggregate-Based Concrete Stress Monitoring via 1D CNN Deep Learning of Raw Impedance Signals\",\"authors\":\"Quoc-Bao Ta,&nbsp;Quang-Quang Pham,&nbsp;Ngoc-Lan Pham,&nbsp;Thanh-Canh Huynh,&nbsp;Jeong-Tae Kim\",\"doi\":\"10.1155/2024/5822653\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n <p>A 1-dimensional convolutional neural network (1D CNN) model is developed to process deep learning of raw impedance signals for smart aggregate (SA)-based concrete stress monitoring. First, the framework of the SA-based stress monitoring using deep learning of raw impedance signals is described. An impedance measurement model is designed for a SA-embedded concrete body under compression. A 1D CNN model is developed for deep learning of raw impedance signals corresponding to various stress levels. Three approaches for concrete stress monitoring are designed to deal with data availability, signal noises, and untrained stress levels. Second, a few SA-embedded concrete cylinders are experimented to measure impedance signals under various stress levels. Finally, the performance of the proposed method is extensively evaluated by investigating the feasibility of the K-fold cross-validation to deal with the data availability and the effects of signal noises and untrained data on the accuracy of stress estimation in the SA-embedded concrete cylinders.</p>\\n </div>\",\"PeriodicalId\":49471,\"journal\":{\"name\":\"Structural Control & Health Monitoring\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-03-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/5822653\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Structural Control & Health Monitoring\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1155/2024/5822653\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Control & Health Monitoring","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/2024/5822653","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

开发了一种一维卷积神经网络(1D CNN)模型,用于处理原始阻抗信号的深度学习,以实现基于智能骨料(SA)的混凝土应力监测。首先,介绍了利用原始阻抗信号深度学习进行基于 SA 的应力监测的框架。为受压的嵌入 SA 的混凝土体设计了阻抗测量模型。开发了一维 CNN 模型,用于深度学习与各种应力水平相对应的原始阻抗信号。针对数据可用性、信号噪声和未经训练的应力水平,设计了三种混凝土应力监测方法。其次,对几个嵌入 SA 的混凝土圆柱体进行了实验,以测量各种应力水平下的阻抗信号。最后,通过研究 K 倍交叉验证处理数据可用性的可行性,以及信号噪声和未经训练的数据对 SA 嵌入式混凝土圆柱体应力估计精度的影响,广泛评估了所提方法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
Smart Aggregate-Based Concrete Stress Monitoring via 1D CNN Deep Learning of Raw Impedance Signals

A 1-dimensional convolutional neural network (1D CNN) model is developed to process deep learning of raw impedance signals for smart aggregate (SA)-based concrete stress monitoring. First, the framework of the SA-based stress monitoring using deep learning of raw impedance signals is described. An impedance measurement model is designed for a SA-embedded concrete body under compression. A 1D CNN model is developed for deep learning of raw impedance signals corresponding to various stress levels. Three approaches for concrete stress monitoring are designed to deal with data availability, signal noises, and untrained stress levels. Second, a few SA-embedded concrete cylinders are experimented to measure impedance signals under various stress levels. Finally, the performance of the proposed method is extensively evaluated by investigating the feasibility of the K-fold cross-validation to deal with the data availability and the effects of signal noises and untrained data on the accuracy of stress estimation in the SA-embedded concrete cylinders.

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
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
期刊最新文献
Vision Transformer–Based Anomaly Detection Method for Offshore Platform Monitoring Data Investigation of the Mechanism of Hidden Defects in Epoxy Asphalt Pavement on Steel Bridge Decks Under Moisture Diffusion Using Nondestructive Detection Techniques Multidamage Detection of Breathing Cracks in Plate-Like Bridges: Experimental and Numerical Study Designing a Distributed Sensing Network for Structural Health Monitoring of Concrete Tunnels: A Case Study Detection of Delamination in Composite Laminate Using Mode Shape Processing Method and YOLOv8
×
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