{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T22:09:03Z","timestamp":1776204543562,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T00:00:00Z","timestamp":1652918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Provincial Key Research and Development program of China","award":["2020C03074"],"award-info":[{"award-number":["2020C03074"]}]},{"name":"Zhejiang Provincial Key Research and Development program of China","award":["LY18F030012"],"award-info":[{"award-number":["LY18F030012"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China (ZJNSF)","award":["2020C03074"],"award-info":[{"award-number":["2020C03074"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China (ZJNSF)","award":["LY18F030012"],"award-info":[{"award-number":["LY18F030012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a new intelligent recognition method for concrete ultrasonic detection based on wavelet packet transform and a convolutional neural network (CNN). To validate the proposed data-based method, a case study is presented where the K-fold cross-validation was adopted to produce the performance analysis and classification experiments. Moreover, three evaluation indicators, precision, recall, and F-score, are calculated for analyzing the classification performance of the trained models. As a result, the obtained four-classifying CNN reaches more than 99% detection accuracy while the lowest recognition accuracy is not less than 92.5% on the testing dataset for the six-classifying CNN model. Compared with the existing stochastic configuration network (SCN) models, the presented method achieves the design objective with better recognition performance. The calculation results of the six-classifying and five-classifying models and related research clearly indicate the remaining challenging tasks for intelligent recognition algorithms in extracting features and classifying mass data from various concrete defects precisely and efficiently.<\/jats:p>","DOI":"10.3390\/s22103863","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T00:18:11Z","timestamp":1653005891000},"page":"3863","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Wavelet Packet Transform and Convolutional Neural Network Method Based Ultrasonic Detection Signals Recognition of Concrete"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2509-7550","authenticated-orcid":false,"given":"Jinhui","family":"Zhao","sequence":"first","affiliation":[{"name":"Zhejiang-Belarus Joint Laboratory of Intelligent Equipment and System for Water Conservancy and Hydropower Safety Monitoring, College of Electrical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyu","family":"Hu","sequence":"additional","affiliation":[{"name":"Zhejiang-Belarus Joint Laboratory of Intelligent Equipment and System for Water Conservancy and Hydropower Safety Monitoring, College of Electrical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China"},{"name":"College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2479-8195","authenticated-orcid":false,"given":"Qichun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.cemconres.2013.07.009","article-title":"A review of ultrasonic coda wave interferometry in concrete","volume":"53","author":"Planes","year":"2013","journal-title":"Cem. Concr. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1111\/mice.12263","article-title":"Deep learning-based crack damage detection using convolutional neural networks","volume":"32","author":"Cha","year":"2017","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1111\/mice.12334","article-title":"Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types","volume":"33","author":"Cha","year":"2018","journal-title":"Comput. Aided Civ. Infrastruct. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2702","DOI":"10.1109\/TUFFC.2012.2511","article-title":"A study of internal defect testing with the Laser-EMAT ultrasonic method","volume":"59","author":"Pei","year":"2012","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ultras.2016.11.003","article-title":"A contactless ultrasonic surface wave approach to characterize distributed cracking damage in concrete","volume":"75","author":"Ham","year":"2017","journal-title":"Ultrasonics"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yan, Y. (2013, January 8\u20139). Ultrasonic testing of the application of the concrete defects. Proceedings of the Conference on Education Technology and Management Science (ICETMS), Nanjing, China.","DOI":"10.2991\/icetms.2013.391"},{"key":"ref_7","first-page":"51","article-title":"Faults diagnosis method for gearboxes based on a 1-D convolutional neural network","volume":"37","author":"Wu","year":"2018","journal-title":"J. Vib. Shock."},{"key":"ref_8","first-page":"571","article-title":"Study on identification and classification of ultrasonic flaw detection signal for composite insulator","volume":"34","author":"Wang","year":"2019","journal-title":"J. Electr. Power"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1784\/insi.2016.58.11.609","article-title":"Concrete strength evaluation in an early-age curing process using SVM with ultrasonic harmonic waves","volume":"58","author":"Oh","year":"2016","journal-title":"Insight"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Saechai, S., Kongprawechnon, W., and Sahamitmongkol, R. (2012, January 20\u201324). Test system for defect detection in construction materials with ultrasonic waves by support vector machine and neural network. Proceedings of the 6th International Conference on Soft Computing and Intelligent Systems, Kobe, Japan.","DOI":"10.1109\/SCIS-ISIS.2012.6505090"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e635","DOI":"10.7717\/peerj-cs.635","article-title":"Ultrasonic based concrete defects identification via wavelet packet transform and GA-BP neural network","volume":"7","author":"Hu","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9284","DOI":"10.1109\/ACCESS.2021.3049448","article-title":"Defect recognition in roncrete ultrasonic detection based on wavelet packet transform and stochastic configuration networks","volume":"9","author":"Zhao","year":"2021","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhao, J., Hu, T., Zheng, R., Ba, P., and Zhang, Q. (2021). Design and performance analysis of an ultrasonic system for health monitoring of concrete structure. Sensors, 21.","DOI":"10.3390\/s21196606"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.autcon.2011.06.012","article-title":"Ultrasonic signal processing methods for detection of defects in concrete pipes","volume":"22","author":"Iyer","year":"2012","journal-title":"Autom. Constr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.1007\/s00170-021-07325-7","article-title":"A review on deep learning in machining and tool monitoring: Methods, opportunities, and challenges","volume":"115","author":"Nasir","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_16","first-page":"11","article-title":"Deep learning in ultrasound imaging","volume":"108","author":"Cohen","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s13755-019-0078-0","article-title":"Towards the classification of heart sounds based on convolutional deep neural network","volume":"7","author":"Demir","year":"2019","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.ultras.2018.12.001","article-title":"Convolutional neural network for ultrasonic weldment flaw classification in noisy conditions","volume":"94","author":"Munir","year":"2019","journal-title":"Ultrasonics"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1134\/S1061830915080045","article-title":"Application of synchronous detection for ultrasonic thickness gauging of concrete articles with a nonuniform structure","volume":"51","author":"Kartashev","year":"2015","journal-title":"Russ. J. Nondestruct. Test."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1007\/s00170-008-1640-0","article-title":"A hybrid signal pre-processing approach in processing ultrasonic signals with noise","volume":"42","author":"Palanisamy","year":"2009","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"125118","DOI":"10.1063\/1.5074152","article-title":"Real time implementation of empirical mode decomposition algorithm for ultrasonic nondestructive testing applications","volume":"89","author":"Tarpara","year":"2018","journal-title":"Rev. Sci. Instrum."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1007\/s00521-012-1305-7","article-title":"Wavelet transform-based feature extraction for ultrasonic flaw signal classification","volume":"24","author":"Yang","year":"2014","journal-title":"Neural Comput. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1007\/s10921-013-0191-y","article-title":"Experimental study on ultrasonic monitoring of splitting failure in reinforced concrete","volume":"32","author":"Rucka","year":"2013","journal-title":"J. Nondestruct. Eval."},{"key":"ref_24","first-page":"012062","article-title":"Performance analysis of a wavelet packet transform applied to concrete ultrasonic detection signals","volume":"Volume 1894","author":"Hu","year":"2021","journal-title":"Proceedings of the International Conference on Intelligent Control, Measurement and Signal Processing 2020"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3466","DOI":"10.1109\/TCYB.2017.2734043","article-title":"Stochastic configuration networks: Fundamentals and algorithms","volume":"47","author":"Wang","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_26","first-page":"40","article-title":"Damage identification of platform structure under random wave conditions based on wavelet packet transformation","volume":"35","author":"Wan","year":"2020","journal-title":"China Offshore Platf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1109\/TSP.2009.2031726","article-title":"Central limit theorems for wavelet packet decompositions of stationary random processes","volume":"58","author":"Atto","year":"2010","journal-title":"IEEE Trans. Signal Proces."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4564","DOI":"10.1038\/s41598-017-04837-9","article-title":"Study on optimal selection of wavelet vanishing moments for ECG denoising","volume":"7","author":"Peng","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1007\/s11517-015-1354-z","article-title":"A signal invariant wavelet function selection algorithm","volume":"54","author":"Garg","year":"2016","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_30","first-page":"94","article-title":"Wavelet packet denoising and feature extraction for flaw echo Signal in ultrasonic testing","volume":"27","author":"Zhang","year":"2006","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1113\/jphysiol.1962.sp006837","article-title":"Receptive fields, binocular interaction and functional architecture in the cat\u2019s visual cortex","volume":"160","author":"Hubel","year":"1962","journal-title":"J. Physiol."},{"key":"ref_32","first-page":"2562","article-title":"Fault diagnosis of accessories for the low voltage conventional circuit breaker based on one-dimensional convolutional neural network","volume":"35","author":"Sun","year":"2020","journal-title":"Trans. China Electrotech. Soc."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hang, J., Zhang, D.X., Chen, P., Zhang, J., and Wang, B. (2019). Classification of plant leaf diseases based on improved convolutional neural network. Sensors, 19.","DOI":"10.3390\/s19194161"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1002\/spe.2687","article-title":"Intelligent sentiment analysis approach using edge computing-based deep learning technique","volume":"50","author":"Sankar","year":"2020","journal-title":"Softw. Pract. Exp."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"65060","DOI":"10.1109\/ACCESS.2020.2985717","article-title":"Predicting ayurveda-based constituent balancing in human body using machine learning methods","volume":"8","author":"Madaan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1049\/el.2020.1574","article-title":"Defect classification based on wavelet packet energy through pulsed alternating current field measurement technique","volume":"56","author":"Ge","year":"2020","journal-title":"Electron. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yadav, S., and Shukla, S. (2016, January 27\u201328). Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. Proceedings of the IEEE 6th International Advance Computing Conference (IACC), Bhimavaram, India.","DOI":"10.1109\/IACC.2016.25"},{"key":"ref_38","first-page":"2423","article-title":"Application of convolutional neural network and kurtosis in fault diagnosis of rolling bearing","volume":"34","author":"Li","year":"2019","journal-title":"J. Aerosp. Power"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Langroudi, H.F., Merkel, C., Syed, H., and Kudithipudi, D. (2019, January 14\u201319). Exploiting randomness in deep learning algorithms. Proceedings of the International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852192"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Epp, T., Svecova, D., and Cha, Y.J. (2018). Semi-automated air-coupled impact-echo method for large-scale parkade structure. Sensors, 18.","DOI":"10.3390\/s18041018"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3863\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:14:54Z","timestamp":1760138094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3863"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,19]]},"references-count":41,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22103863"],"URL":"https:\/\/doi.org\/10.3390\/s22103863","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,19]]}}}