基于深度学习自动消除无效冲击回波信号以检测混凝土桥面分层

IF 6.2 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
{"title":"基于深度学习自动消除无效冲击回波信号以检测混凝土桥面分层","authors":"","doi":"10.1016/j.dibe.2024.100521","DOIUrl":null,"url":null,"abstract":"<div><p>The impact-echo (IE) method is effective for evaluating invisible defects. However, it might return misleading results when its signals are invalid. This challenge aggravates when the tests are conducted using robotic devices that automatically collect massive data. This study proposes an automatic method to eliminate invalid signals based on the ResNet model. First, the signals are visualized into two-dimensional images as the input for ResNet. The input data can then be classified into valid and invalid data via the ResNet model, which is trained with 11,290 signals and tested with 5664 signals. Finally, defects can be detected using the dominant frequencies of the valid-class data. A case study with IE data from two concrete bridges was employed to validate the feasibility of the proposed approach. The results indicate that the method can achieve an average accuracy of 90.6% for eliminating invalid signals and significantly improve the IE test accuracy.</p></div>","PeriodicalId":34137,"journal":{"name":"Developments in the Built Environment","volume":null,"pages":null},"PeriodicalIF":6.2000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666165924002023/pdfft?md5=dc4d7715b291507525e44868ec8a1ea9&pid=1-s2.0-S2666165924002023-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Automatic elimination of invalid impact-echo signals for detecting delamination in concrete bridge decks based on deep learning\",\"authors\":\"\",\"doi\":\"10.1016/j.dibe.2024.100521\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The impact-echo (IE) method is effective for evaluating invisible defects. However, it might return misleading results when its signals are invalid. This challenge aggravates when the tests are conducted using robotic devices that automatically collect massive data. This study proposes an automatic method to eliminate invalid signals based on the ResNet model. First, the signals are visualized into two-dimensional images as the input for ResNet. The input data can then be classified into valid and invalid data via the ResNet model, which is trained with 11,290 signals and tested with 5664 signals. Finally, defects can be detected using the dominant frequencies of the valid-class data. A case study with IE data from two concrete bridges was employed to validate the feasibility of the proposed approach. The results indicate that the method can achieve an average accuracy of 90.6% for eliminating invalid signals and significantly improve the IE test accuracy.</p></div>\",\"PeriodicalId\":34137,\"journal\":{\"name\":\"Developments in the Built Environment\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666165924002023/pdfft?md5=dc4d7715b291507525e44868ec8a1ea9&pid=1-s2.0-S2666165924002023-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Developments in the Built Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666165924002023\",\"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":"Developments in the Built Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666165924002023","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

冲击回波(IE)方法对评估隐形缺陷很有效。然而,当信号无效时,它可能会返回误导性结果。当使用自动收集大量数据的机器人设备进行测试时,这一挑战就会加剧。本研究提出了一种基于 ResNet 模型的自动消除无效信号的方法。首先,将信号可视化为二维图像,作为 ResNet 的输入。然后,通过 ResNet 模型将输入数据分为有效数据和无效数据,该模型用 11,290 个信号进行了训练,并用 5664 个信号进行了测试。最后,可以利用有效类数据的主频来检测缺陷。通过对两座混凝土桥梁的 IE 数据进行案例研究,验证了所提方法的可行性。结果表明,该方法消除无效信号的平均准确率可达 90.6%,显著提高了 IE 测试的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic elimination of invalid impact-echo signals for detecting delamination in concrete bridge decks based on deep learning

The impact-echo (IE) method is effective for evaluating invisible defects. However, it might return misleading results when its signals are invalid. This challenge aggravates when the tests are conducted using robotic devices that automatically collect massive data. This study proposes an automatic method to eliminate invalid signals based on the ResNet model. First, the signals are visualized into two-dimensional images as the input for ResNet. The input data can then be classified into valid and invalid data via the ResNet model, which is trained with 11,290 signals and tested with 5664 signals. Finally, defects can be detected using the dominant frequencies of the valid-class data. A case study with IE data from two concrete bridges was employed to validate the feasibility of the proposed approach. The results indicate that the method can achieve an average accuracy of 90.6% for eliminating invalid signals and significantly improve the IE test accuracy.

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
CiteScore
7.40
自引率
1.20%
发文量
31
审稿时长
22 days
期刊介绍: Developments in the Built Environment (DIBE) is a recently established peer-reviewed gold open access journal, ensuring that all accepted articles are permanently and freely accessible. Focused on civil engineering and the built environment, DIBE publishes original papers and short communications. Encompassing topics such as construction materials and building sustainability, the journal adopts a holistic approach with the aim of benefiting the community.
×
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