Crack identification in concrete,using digital image correlation and neural network

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外文摘要：In engineering applications,concrete crack monitoring is very important.Traditional methods are of low efficiency,low accuracy,have poor timeliness,and are applicable in only a limited number of scenarios.Therefore,more comprehensive detection of concrete damage under different scenarios is of high value for practical engineering applications.Digital image correlation(DIC)technology can provide a large amount of experimental data,and neural network(NN)can process very rich data.Therefore,NN,including convolutional neural networks(CNN)and back propagation neural networks(BP),can be combined with DIC technology to analyze experimental data of three-point bending of plain concrete and four-point bending of reinforced concrete.In addition,strain parameters can be used for training,and displacement parameters can be added for comprehensive consideration.The data obtained by DIC technology are grouped for training,and the recognition results of NN show that the combination of strain and displacement parameters,i.e.,the response of specimen surface and whole body,can make results more objective and comprehensive.The identification results obtained by CNN and BP show that these technologies can accurately identify cracks.The identification results for reinforced concrete specimens are less affected by noise than those of plain concrete specimens.CNN is more convenient because it can identify some features directly from images,recognizing the cracks formed by macro development.BP can issue early warning of the microscopic cracks,but it requires a large amount of data and computation.It can be seen that CNN is more intuitive and efficient in image processing,and is suitable when low accuracy is adequate,while BP is suitable for occasions with greater accuracy requirements.The two tools have advantages in different situations,and together they can play an important role in engineering monitoring.

外文关键词：

digital image correlationconvolutional neural networkback propagation neural neural networkdamage detectionconcrete

作者：

Jingyi WANG、Dong LEI、Kaiyang ZHOU、Jintao HE、Feipeng ZHU、Pengxiang BAI

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作者单位：

College of Mechanics and Materials,Hohai University,Nanjing 211100,China

基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaResearch and Innovation Plan for Postgraduates in Jiangsu Province

项目编号：

U176520451679078KYCX21_0458

出版年：

2024

DOI：

10.1007/s11709-024-1013-2

结构与土木工程前沿

高等教育出版社

结构与土木工程前沿

CSTPCDEI

影响因子：0.082

ISSN：2095-2430

年,卷(期)：2024.(4)