Nonlinear ultrasonic concrete crack identification with deep learning based on time-frequency image

非线性系统 深度学习 人工智能 超声波传感器 时频分析 计算机科学 傅里叶变换 鉴定(生物学) 模式识别(心理学) 连续小波变换 小波变换 声学 小波 计算机视觉 数学 离散小波变换 数学分析 物理 生物 滤波器(信号处理) 量子力学 植物
作者
Jianfeng Liu,Kui Wang,Mingjie Zhao,Yongjiang Chen
出处
期刊:Nondestructive Testing and Evaluation [Taylor & Francis]
卷期号:39 (5): 1225-1249 被引量:10
标识
DOI:10.1080/10589759.2023.2250513
摘要

ABSTRACTBy combining time-frequency images and deep learning models, the nonlinear ultrasound signals can be classified, detected, and predicted, using the nonlinear coefficient as a fundamental label for training deep learning models. This integrated approach enables quantitative identification and real-time monitoring of concrete damage, promoting the widespread adoption of nonlinear ultrasonic techniques in engineering applications. As a basis, the relationship between damage variations and nonlinear coefficients is discussed by performing nonlinear ultrasonic damage testing on concrete specimens with different crack lengths and angles. The testing signals are converted into time-frequency images using the short-time Fourier transform and the continuous wavelet transform, and both types of images are combined for data augmentation and input into the deep learning model for training, with nonlinear coefficients serving as labels for the time-frequency images. The MobileNetV2, VGG16, and ResNet18 deep learning models are trained separately on time-frequency image datasets for the length specimens, the angle specimens, and the length-angle specimens, and the performance of the different models is evaluated and compared. The results show that all three models have accuracy rates above 94%, indicating good identification performance. Finally, with the example, the nonlinear coefficients of the testing signals are compared with the labels of the nonlinear coefficients in the time-frequency images identified by the deep learning model, which confirms the high accuracy of damage identification by the deep learning model.KEYWORDS: Time-frequency imagedeep learningnonlinear ultrasoundnonlinear coefficientconcrete AcknowledgmentsThe authors appreciate everyone who have contributed to the completion of this study.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe research is funded by the Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-K202100705), the Chongqing Talent Program "Package System" Project (Grant No. cstc2022ycjh-bgzxm0080), the Chongqing Water Conservancy Science and Technology Project (Grant No. CQSLK-2022002) and the Research and Innovation Program for Graduate Students in Chongqing (Grant No. CYB22236).
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
lihuahui发布了新的文献求助10
刚刚
ED应助RosyBai采纳,获得10
1秒前
lotus完成签到 ,获得积分10
1秒前
仿生人发布了新的文献求助10
1秒前
3秒前
4秒前
4秒前
嘿撒完成签到,获得积分10
5秒前
tinneywu发布了新的文献求助10
5秒前
充电宝应助烛光采纳,获得10
7秒前
7秒前
errui发布了新的文献求助10
8秒前
椰子冻发布了新的文献求助10
8秒前
9秒前
miao发布了新的文献求助10
11秒前
多多发布了新的文献求助10
12秒前
14秒前
江峰发布了新的文献求助10
14秒前
15秒前
fan完成签到,获得积分10
15秒前
无花果应助结实的安梦采纳,获得10
16秒前
16秒前
17秒前
快乐完成签到,获得积分10
18秒前
wuhu发布了新的文献求助10
19秒前
19秒前
zzzzz发布了新的文献求助10
19秒前
Evelvon发布了新的文献求助10
20秒前
21秒前
科研通AI2S应助lihuahui采纳,获得10
22秒前
领导范儿应助wt1123采纳,获得10
22秒前
MrRen发布了新的文献求助10
22秒前
23秒前
江峰完成签到,获得积分10
24秒前
小白白发布了新的文献求助10
24秒前
26秒前
Hello应助biyeshunli采纳,获得10
26秒前
Evelvon完成签到,获得积分10
26秒前
junkook发布了新的文献求助200
26秒前
santrue发布了新的文献求助100
27秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Effective Learning and Mental Wellbeing 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3976126
求助须知:如何正确求助?哪些是违规求助? 3520340
关于积分的说明 11202586
捐赠科研通 3256847
什么是DOI,文献DOI怎么找? 1798509
邀请新用户注册赠送积分活动 877645
科研通“疑难数据库(出版商)”最低求助积分说明 806516