{"title":"基于稀疏最小二乘支持向量回归的混凝土三维声发射源定位实验研究","authors":"","doi":"10.32548/2023.me-04258","DOIUrl":null,"url":null,"abstract":"In order to further prove the effectiveness of the sparse least-squares support vector regression (S-LS-SVR) method in damage detection, the authors used the S-LS-SVR model to locate actual damage sources of concrete. The data from acoustic emission testing (AE) are generated and filtered by the pullout test of reinforcement in concrete, and the three-dimensional coordinates of real-time damage sources in the failure process are provided through the model. The S-LS-SVR method is compared with the Newton iterative method and improved exhaustive method for positioning speed, positioning data utilization, and positioning accuracy. The results show that S-LS-SVR is superior to the two other time difference of arrival–based positioning methods in positioning speed, positioning data utilization, and positioning accuracy (data utilization is slightly lower than the improved exhaustive method). The location method based on S-LS-SVR provides the possibility for the application of AE technology in intelligent damage location of bridges, dams, and other service structures.","PeriodicalId":49876,"journal":{"name":"Materials Evaluation","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Experimental Study On 3D Acoustic Emission Source Location of Concrete Based On Sparse Least-Squares Support Vector Regression\",\"authors\":\"\",\"doi\":\"10.32548/2023.me-04258\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to further prove the effectiveness of the sparse least-squares support vector regression (S-LS-SVR) method in damage detection, the authors used the S-LS-SVR model to locate actual damage sources of concrete. The data from acoustic emission testing (AE) are generated and filtered by the pullout test of reinforcement in concrete, and the three-dimensional coordinates of real-time damage sources in the failure process are provided through the model. The S-LS-SVR method is compared with the Newton iterative method and improved exhaustive method for positioning speed, positioning data utilization, and positioning accuracy. The results show that S-LS-SVR is superior to the two other time difference of arrival–based positioning methods in positioning speed, positioning data utilization, and positioning accuracy (data utilization is slightly lower than the improved exhaustive method). The location method based on S-LS-SVR provides the possibility for the application of AE technology in intelligent damage location of bridges, dams, and other service structures.\",\"PeriodicalId\":49876,\"journal\":{\"name\":\"Materials Evaluation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2023-03-01\",\"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/10.32548/2023.me-04258\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Evaluation","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.32548/2023.me-04258","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}
Experimental Study On 3D Acoustic Emission Source Location of Concrete Based On Sparse Least-Squares Support Vector Regression
In order to further prove the effectiveness of the sparse least-squares support vector regression (S-LS-SVR) method in damage detection, the authors used the S-LS-SVR model to locate actual damage sources of concrete. The data from acoustic emission testing (AE) are generated and filtered by the pullout test of reinforcement in concrete, and the three-dimensional coordinates of real-time damage sources in the failure process are provided through the model. The S-LS-SVR method is compared with the Newton iterative method and improved exhaustive method for positioning speed, positioning data utilization, and positioning accuracy. The results show that S-LS-SVR is superior to the two other time difference of arrival–based positioning methods in positioning speed, positioning data utilization, and positioning accuracy (data utilization is slightly lower than the improved exhaustive method). The location method based on S-LS-SVR provides the possibility for the application of AE technology in intelligent damage location of bridges, dams, and other service structures.
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.