Deep Learning-Based Inversion Analysis of Interlayer Contact Parameters in Pavement Structures

Deep Learning-Based Inversion Analysis of Interlayer Contact Parameters in Pavement Structures | 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 Deep Learning-Based Inversion Analysis of Interlayer Contact Parameters in Pavement Structures Zejun Han, Ming Ma, Ziyan Yan, Xing Gong, Hao Zhang, Linqing Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7990127/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 Accurate evaluation of interlayer contact conditions is essential for understanding pavement performance, yet conventional inversion methods often fail to capture these parameters effectively. This study presents a deep learning–based framework that integrates a Spectral Element Method (SEM) forward model with an enhanced one-dimensional LeNet-5 Convolutional Neural Network (CNN) for simultaneous inversion of horizontal and vertical contact parameters, elastic modulus, and layer thickness using Falling Weight Deflectometer (FWD) data. The supervised CNN achieved high accuracy, with average relative errors of 2.63% and 1.17% for horizontal and vertical contact parameters, respectively. To address limited abnormal data, a convolutional autoencoder was employed for unsupervised pavement state evaluation. Field validation confirmed the model’s ability to distinguish pre- and post-grouting conditions through reconstruction error and structural similarity analysis. The proposed approach offers a robust, non-destructive, and intelligent solution for quantitative inversion and qualitative assessment of pavement structures. Interlayer contact parameters Inversion analysis Deep learning Spectral element method Falling weight deflectometer 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7990127","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542207487,"identity":"cf5047ae-b451-4f3b-8578-7a33cbeeb418","order_by":0,"name":"Zejun Han","email":"","orcid":"","institution":"Guangdong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zejun","middleName":"","lastName":"Han","suffix":""},{"id":542207488,"identity":"e2163956-9a74-4b7d-946c-97777917c72f","order_by":1,"name":"Ming Ma","email":"","orcid":"","institution":"Guangdong University of 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Structures","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Interlayer contact parameters, Inversion analysis, Deep learning, Spectral element method, Falling weight 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This study presents a deep learning\u0026ndash;based framework that integrates a Spectral Element Method (SEM) forward model with an enhanced one-dimensional LeNet-5 Convolutional Neural Network (CNN) for simultaneous inversion of horizontal and vertical contact parameters, elastic modulus, and layer thickness using Falling Weight Deflectometer (FWD) data. The supervised CNN achieved high accuracy, with average relative errors of 2.63% and 1.17% for horizontal and vertical contact parameters, respectively. To address limited abnormal data, a convolutional autoencoder was employed for unsupervised pavement state evaluation. Field validation confirmed the model\u0026rsquo;s ability to distinguish pre- and post-grouting conditions through reconstruction error and structural similarity analysis. The proposed approach offers a robust, non-destructive, and intelligent solution for quantitative inversion and qualitative assessment of pavement structures.\u003c/p\u003e","manuscriptTitle":"Deep Learning-Based Inversion Analysis of Interlayer Contact Parameters in Pavement Structures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 22:59:37","doi":"10.21203/rs.3.rs-7990127/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"ec90291c-08e5-4ecb-bb11-ef2283ea8b6f","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T21:24:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 22:59:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7990127","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7990127","identity":"rs-7990127","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}