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dc.contributor.authorZhao, J.
dc.contributor.authorHu, T.
dc.contributor.authorZheng, R.
dc.contributor.authorBa, P.
dc.contributor.authorMei, C.
dc.contributor.authorZhang, Qichun
dc.date.accessioned2021-01-13T22:25:56Z
dc.date.accessioned2021-01-21T12:30:26Z
dc.date.available2021-01-13T22:25:56Z
dc.date.available2021-01-21T12:30:26Z
dc.date.issued2021-01
dc.identifier.citationZhao J, Hu T, Zheng R (2021) Defect recognition in concrete ultrasonic detection based on wavelet packet transform and stochastic configuration networks. IEEE Access. 9: 9284-9295 .en_US
dc.identifier.urihttp://hdl.handle.net/10454/18325
dc.descriptionYesen_US
dc.description.abstractAiming to detect concrete defects, we propose a new identification method based on stochastic configuration networks. The presented model has been trained by time-domain and frequency-domain features which are extracted from filtering and decomposing ultrasonic detection signals. This method was applied to ultrasonic detection data collected from 5 mm, 7 mm, and 9 mm penetrating holes in C30 class concrete. In particular, wavelet packet transform (WPT) was then used to decompose the detected signals, thus the information in different frequency bands can be obtained. Based on the data from the fundamental frequency nodes of the detection signals, we calculated the means, standard deviations, kurtosis coefficients, skewness coefficients and energy ratios to characterize the detection signals. We also analyzed their typical statistical features to assess the complexity of identifying these signals. Finally, we used the stochastic configuration networks (SCNs) algorithm to embed four-fold cross-validation for constructing the recognition model. Based upon the experimental results, the performance of the presented model has been validated and compared with the genetic algorithm based BP neural network model, where the comparison shows that the SCNs algorithm has superior generalization abilities, better fitting abilities, and higher recognition accuracy for recognizing defect signals. In addition, the test and analysis results show that the proposed method is feasible and effective in detecting concrete hole defects.en_US
dc.description.sponsorshipThis work was supported in part by the Zhejiang Provincial Natural Science Foundation (ZJNSF) project under Grant (No. LY18F030012), the National Natural Science Foundation of China projects (NSFC) under Grant (No. 61403356, 61573311).en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1109/ACCESS.2021.3049448en_US
dc.rights© The Authors 2020. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/en_US
dc.subjectConcrete defectsen_US
dc.subjectUltrasonic detectionen_US
dc.subjectWavelet packet transformen_US
dc.subjectStochastic configuration networksen_US
dc.subjectPattern recognitionen_US
dc.titleDefect recognition in concrete ultrasonic detection based on wavelet packet transform and stochastic configuration networksen_US
dc.status.refereedYesen_US
dc.date.Accepted2020-12-29
dc.date.application2021-01-05
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.date.updated2021-01-13T22:26:04Z
refterms.dateFOA2021-01-21T14:34:54Z
dc.openaccess.statusGolden_US


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