A novel spatiotemporal 3D CNN framework with multi-task learning for efficient structural damage detection

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Sadeq Kord, Touraj Taghikhany, Mohammad Akbari
{"title":"A novel spatiotemporal 3D CNN framework with multi-task learning for efficient structural damage detection","authors":"Sadeq Kord, Touraj Taghikhany, Mohammad Akbari","doi":"10.1177/14759217231206178","DOIUrl":null,"url":null,"abstract":"In recent years, convolutional neural networks (CNNs) have demonstrated promising results in detecting structural damage. However, their architectures often overlook spatial and temporal effects simultaneously. This limitation can result in the loss of valuable information and an incapability to fully capture the complexity of the data, ultimately leading to reduced accuracy and suboptimal performance. This study proposes an intuitive three-dimensional CNN architecture that takes into account vibration history along with sensor spatial relations based on their relative positions. Furthermore, a multi-task learning (MTL) approach is suggested, which is a powerful approach for performing multiple tasks with a single network. The proposed 3D CNN method has been employed to detect single and double damage cases in an experimental steel frame through conventional classification alongside the transfer learning (TL). Moreover, MTL is used to detect single and double damage scenarios with a single unified network, which evaluates damage presence in separate tasks. The 3D CNN fulfilled state-of-the-art performance and 100% accuracy in detecting structural damage in almost all experiments. Additionally, the MTL model achieved promising results even in the presence of severe imbalanced classes of data. Furthermore, it was observed that the utilization of TL resulted in a notable reduction of computation time by 68% and the number of trainable parameters by 90% with the same level of accuracy in double-damage cases.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":null,"pages":null},"PeriodicalIF":5.7000,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Health Monitoring-An International Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/14759217231206178","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

In recent years, convolutional neural networks (CNNs) have demonstrated promising results in detecting structural damage. However, their architectures often overlook spatial and temporal effects simultaneously. This limitation can result in the loss of valuable information and an incapability to fully capture the complexity of the data, ultimately leading to reduced accuracy and suboptimal performance. This study proposes an intuitive three-dimensional CNN architecture that takes into account vibration history along with sensor spatial relations based on their relative positions. Furthermore, a multi-task learning (MTL) approach is suggested, which is a powerful approach for performing multiple tasks with a single network. The proposed 3D CNN method has been employed to detect single and double damage cases in an experimental steel frame through conventional classification alongside the transfer learning (TL). Moreover, MTL is used to detect single and double damage scenarios with a single unified network, which evaluates damage presence in separate tasks. The 3D CNN fulfilled state-of-the-art performance and 100% accuracy in detecting structural damage in almost all experiments. Additionally, the MTL model achieved promising results even in the presence of severe imbalanced classes of data. Furthermore, it was observed that the utilization of TL resulted in a notable reduction of computation time by 68% and the number of trainable parameters by 90% with the same level of accuracy in double-damage cases.
基于多任务学习的新型时空三维CNN框架用于结构损伤检测
近年来,卷积神经网络(cnn)在检测结构损伤方面表现出了良好的效果。然而,他们的建筑往往同时忽略了空间和时间的影响。这种限制可能导致丢失有价值的信息,并且无法完全捕获数据的复杂性,最终导致准确性降低和性能次优。本研究提出了一种直观的三维CNN架构,该架构考虑了振动历史以及基于传感器相对位置的空间关系。此外,本文还提出了一种多任务学习(MTL)方法,该方法是在单个网络中执行多个任务的有效方法。本文提出的三维CNN方法通过传统的分类和迁移学习(TL)来检测实验钢架的单损伤和双损伤情况。此外,MTL被用于用一个统一的网络检测单损伤和双损伤场景,在不同的任务中评估损伤存在。在几乎所有的实验中,3D CNN都达到了最先进的性能和100%的结构损伤检测准确率。此外,即使在存在严重不平衡的数据类别的情况下,MTL模型也取得了令人满意的结果。此外,我们观察到,在双重损伤情况下,使用TL可以显著减少68%的计算时间和90%的可训练参数数量,并且具有相同的精度水平。
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 求助全文
来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信