Real-time Monitoring of Urban Roadway Health: Utilizing GPR Techniques for Early Detection and Classification of Subsurface Cavity Diseases

Real-time Monitoring of Urban Roadway Health: Utilizing GPR Techniques for Early Detection and Classification of Subsurface Cavity Diseases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Real-time Monitoring of Urban Roadway Health: Utilizing GPR Techniques for Early Detection and Classification of Subsurface Cavity Diseases Rohit Shrestha, Zhihou Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4244789/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The effectiveness of ground penetrating radar (GPR) in identifying and categorizing diseases that occur underground beneath the surfaces of urban roads is investigated in this study. Both 2D and 3D forward modeling use simulation with the GprMax program to show the response characteristics of common cavity illnesses, which facilitates interpretation in practical situations. The cavity morphology classification accuracy is improved to 90.5% by using convolutional neural networks (CNNs), specifically transfer learning with AlexNet. This method outperforms existing approaches even with minimal data. Four primary types are identified from an analysis of 1965 subsurface cavity data: hollow bodies, empty bodies, loose bodies, and water-rich bodies. These categories are important for evaluating road risks such as voids and subsidence. However, it is still difficult to interpret picture features linked to cavity diseases accurately because of a variety of elements, such as anthropogenic, environmental, and geological influences. However, the accurate interpretation and recognition of image features related to cavity diseases remain challenging. Moreover, there are various factors involved in the formation of underground diseases and cavities, including geological and environmental factors, physical and chemical properties of the geotechnical materials, anthropogenic engineering activity and social population or commercial effects. Ground Penetrating Radar Cavities Diseases GprMax AlexNet Inflatable Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4244789","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291145803,"identity":"b876437d-7e18-4a16-a90b-b7e70b8b6fdf","order_by":0,"name":"Rohit Shrestha","email":"data:image/png;base64,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","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Rohit","middleName":"","lastName":"Shrestha","suffix":""},{"id":291145804,"identity":"0659a7da-f753-4c08-8f87-96ff180a0c79","order_by":1,"name":"Zhihou Zhang","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Zhihou","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-04-10 03:16:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4244789/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4244789/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62916470,"identity":"5c962d58-984b-472c-8636-e003a83ae21c","added_by":"auto","created_at":"2024-08-21 04:24:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1489965,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript111.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4244789/v1_covered_c7f2bcd6-321f-45bd-9db6-b027714c8a37.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Real-time Monitoring of Urban Roadway Health: Utilizing GPR Techniques for Early Detection and Classification of Subsurface Cavity Diseases","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ground Penetrating Radar, Cavities, Diseases, GprMax, AlexNet, Inflatable","lastPublishedDoi":"10.21203/rs.3.rs-4244789/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4244789/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe effectiveness of ground penetrating radar (GPR) in identifying and categorizing diseases that occur underground beneath the surfaces of urban roads is investigated in this study. 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