{"title":"浅层路基缺陷的雷达前向建模和智能识别","authors":"","doi":"10.1016/j.trgeo.2024.101385","DOIUrl":null,"url":null,"abstract":"<div><div>Geological radar is the primary nondestructive testing method for evaluating shallow subgrade defects. However, the radar atlas contains a large amount of information, and the efficiency of manual data interpretation and processing is low. In this study, the characteristics of radar maps of different defects were analyzed via forward simulation using finite-difference time-domain technology. The instantaneous characteristic information of different defect maps was integrated using map post-processing technology to improve recognition and translation accuracy. Finally, the convolution neural network algorithm was used to conduct data recognition to achieve the intelligent recognition of subgrade defects with an average detection accuracy of 73.93 % based on the radar subgrade defect atlas dataset, and the results were practically verified. The results show that the developed approach can accurately distinguish subgrade shallow defect information in the radar atlas. This approach is useful for accurate and efficient identification of latent highway defects.</div></div>","PeriodicalId":56013,"journal":{"name":"Transportation Geotechnics","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Radar forward modeling and intelligent identification of shallow subgrade defects\",\"authors\":\"\",\"doi\":\"10.1016/j.trgeo.2024.101385\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Geological radar is the primary nondestructive testing method for evaluating shallow subgrade defects. However, the radar atlas contains a large amount of information, and the efficiency of manual data interpretation and processing is low. In this study, the characteristics of radar maps of different defects were analyzed via forward simulation using finite-difference time-domain technology. The instantaneous characteristic information of different defect maps was integrated using map post-processing technology to improve recognition and translation accuracy. Finally, the convolution neural network algorithm was used to conduct data recognition to achieve the intelligent recognition of subgrade defects with an average detection accuracy of 73.93 % based on the radar subgrade defect atlas dataset, and the results were practically verified. The results show that the developed approach can accurately distinguish subgrade shallow defect information in the radar atlas. This approach is useful for accurate and efficient identification of latent highway defects.</div></div>\",\"PeriodicalId\":56013,\"journal\":{\"name\":\"Transportation Geotechnics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Geotechnics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S221439122400206X\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Geotechnics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221439122400206X","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Radar forward modeling and intelligent identification of shallow subgrade defects
Geological radar is the primary nondestructive testing method for evaluating shallow subgrade defects. However, the radar atlas contains a large amount of information, and the efficiency of manual data interpretation and processing is low. In this study, the characteristics of radar maps of different defects were analyzed via forward simulation using finite-difference time-domain technology. The instantaneous characteristic information of different defect maps was integrated using map post-processing technology to improve recognition and translation accuracy. Finally, the convolution neural network algorithm was used to conduct data recognition to achieve the intelligent recognition of subgrade defects with an average detection accuracy of 73.93 % based on the radar subgrade defect atlas dataset, and the results were practically verified. The results show that the developed approach can accurately distinguish subgrade shallow defect information in the radar atlas. This approach is useful for accurate and efficient identification of latent highway defects.
期刊介绍:
Transportation Geotechnics is a journal dedicated to publishing high-quality, theoretical, and applied papers that cover all facets of geotechnics for transportation infrastructure such as roads, highways, railways, underground railways, airfields, and waterways. The journal places a special emphasis on case studies that present original work relevant to the sustainable construction of transportation infrastructure. The scope of topics it addresses includes the geotechnical properties of geomaterials for sustainable and rational design and construction, the behavior of compacted and stabilized geomaterials, the use of geosynthetics and reinforcement in constructed layers and interlayers, ground improvement and slope stability for transportation infrastructures, compaction technology and management, maintenance technology, the impact of climate, embankments for highways and high-speed trains, transition zones, dredging, underwater geotechnics for infrastructure purposes, and the modeling of multi-layered structures and supporting ground under dynamic and repeated loads.