{"title":"Intelligent enhancement and identification of pipeline hyperbolic signal in 3D ground penetrating radar data","authors":"Yonggang Shen, Guoxuan Ye, Tuqiao Zhang, Tingchao Yu, Yiping Zhang, Zhenwei Yu","doi":"10.1016/j.autcon.2024.105902","DOIUrl":null,"url":null,"abstract":"Concealed pipeline maintenance in aging residential areas faces a key challenge of discrepancies between existing data and reality. Ground-penetrating radar with dense, high-speed 3D monitoring capabilities can provide massive data, but effective analysis is difficult due to the presence of irrelevant information. To accurately extract target information, this paper first proposes a 3D data array block concept, which enhances the feature relevance of target data blocks while expanding the data volume. An energy density window method is also proposed to enhance horizontal cross-sectional pipeline signals. Furthermore, a model named PR3DCNN for pipeline recognition is developed based on 3D convolutional neural networks and residual modules. Experimental results demonstrate that PR3DCNN has a classification accuracy of 0.871 for pipelines. After strengthening with 3D data array blocks and the energy density window, the PR-EDW-B model achieves an accuracy of 0.900, and can also classify the pipeline material and calculate its orientation.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"29 1","pages":""},"PeriodicalIF":9.6000,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.autcon.2024.105902","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Concealed pipeline maintenance in aging residential areas faces a key challenge of discrepancies between existing data and reality. Ground-penetrating radar with dense, high-speed 3D monitoring capabilities can provide massive data, but effective analysis is difficult due to the presence of irrelevant information. To accurately extract target information, this paper first proposes a 3D data array block concept, which enhances the feature relevance of target data blocks while expanding the data volume. An energy density window method is also proposed to enhance horizontal cross-sectional pipeline signals. Furthermore, a model named PR3DCNN for pipeline recognition is developed based on 3D convolutional neural networks and residual modules. Experimental results demonstrate that PR3DCNN has a classification accuracy of 0.871 for pipelines. After strengthening with 3D data array blocks and the energy density window, the PR-EDW-B model achieves an accuracy of 0.900, and can also classify the pipeline material and calculate its orientation.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.