{"title":"Research on Ultrasonic Testing with Wavelet Packet Analysis for Shotcrete","authors":"Fei Yao, Y. Cao","doi":"10.32548/2021.me-04217","DOIUrl":null,"url":null,"abstract":"Shotcrete structures are widely used in tunnel engineering. Quality inspection is difficult, and the traditional ultrasonic testing (UT) method based on first arrival velocity has limitations. In this paper, shotcrete-rock specimens were made in a laboratory and evaluated using UT. Wavelet packet decomposition is introduced for better frequency analysis of the condition evaluation. Two methods, including calculation of the energy eigenvalues and machine learning, are used to describe the contact quality at the interface between the shotcrete and rock. The relative energy eigenvalue increases with the gradual reduction of contact quality, which can become a quantitative index of the contact quality. Machine learning performed well in the rapid recognition of discontinuities in the multiple-classification models. Both methods based on wavelet packet decomposition achieved good results in identifying discontinuities and have the potential to be used in practical engineering applications.","PeriodicalId":49876,"journal":{"name":"Materials Evaluation","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Evaluation","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.32548/2021.me-04217","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0
Abstract
Shotcrete structures are widely used in tunnel engineering. Quality inspection is difficult, and the traditional ultrasonic testing (UT) method based on first arrival velocity has limitations. In this paper, shotcrete-rock specimens were made in a laboratory and evaluated using UT. Wavelet packet decomposition is introduced for better frequency analysis of the condition evaluation. Two methods, including calculation of the energy eigenvalues and machine learning, are used to describe the contact quality at the interface between the shotcrete and rock. The relative energy eigenvalue increases with the gradual reduction of contact quality, which can become a quantitative index of the contact quality. Machine learning performed well in the rapid recognition of discontinuities in the multiple-classification models. Both methods based on wavelet packet decomposition achieved good results in identifying discontinuities and have the potential to be used in practical engineering applications.
期刊介绍:
Materials Evaluation publishes articles, news and features intended to increase the NDT practitioner’s knowledge of the science and technology involved in the field, bringing informative articles to the NDT public while highlighting the ongoing efforts of ASNT to fulfill its mission. M.E. is a peer-reviewed journal, relying on technicians and researchers to help grow and educate its members by providing relevant, cutting-edge and exclusive content containing technical details and discussions. The only periodical of its kind, M.E. is circulated to members and nonmember paid subscribers. The magazine is truly international in scope, with readers in over 90 nations. The journal’s history and archive reaches back to the earliest formative days of the Society.