Exploring the Hydraulic Properties of Unsaturated Soil Using Deep Learning and Digital Imaging Measurement

Exploring the Hydraulic Properties of Unsaturated Soil Using Deep Learning and Digital Imaging Measurement | 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 Exploring the Hydraulic Properties of Unsaturated Soil Using Deep Learning and Digital Imaging Measurement Yanni Huang, Zhoujie Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3855462/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 aim of this work is to improve the accuracy of the traditional Unsaturated Soil Hydraulic Property (USHP) analysis model. To this end, this work combines digital imaging measurement and Deep Learning technology to analyze the USHP. Firstly, the research status of basic characteristics of unsaturated soil and the application status of Deep Learning is analyzed. The second step examines the impact of soil specimens’ physical properties on their hydraulic properties. This includes acquiring hydraulic parameters and the Soil-Water Characteristic Curve (SWCC) based on a full-surface digital imaging measurement. Finally, an SHP model based on the Backpropagation Neural Network (BPNN) is implemented, trained, and validated. The results show that the proposed model’s predicted SWCC is consistent with the experimental phenomena of previous studies. Also, the proposed USHP BPNN model uses the Levenberg Marquardt (LM) algorithm to achieve the best results, with shorter computational time and less noise than other algorithms. Lastly, the results of the proposed model’s various parameters help determine that the optimal number of neurons in the hidden layer is ten. The proposed model’s error is more minor than several literature models thanks to considering the correlation between physical parameters like soil particle size and SHP. Overall, the BPNN is used to model the soil’s physical and hydraulic parameters and has shown excellent performance. The proposed USHP BPNN model helps streamline the traditional soil correlation coefficient estimation. Deep Learning Neural network Digital imaging measurement Soil-Water Characteristic Curve Physical parameters Full Text 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. 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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-3855462","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506248887,"identity":"950b94a2-56cd-4a59-9678-2fd2b4ea89f1","order_by":0,"name":"Yanni Huang","email":"","orcid":"","institution":"Xi'an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yanni","middleName":"","lastName":"Huang","suffix":""},{"id":506248888,"identity":"c19763bc-ea12-42be-8a97-2671c718f33c","order_by":1,"name":"Zhoujie Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYLACxgYgbmZgfAATkCBWC7MBiMNDtBYgYJMgSovB8bOHX/7cYZPY3M58rJqn5rC8PQPzwds8DHZ5OLWcyUuzkDyTltjYzJZ2c8axw4Y9DGzJ1jwMycW4tJgdyDEzMGw7DNTCY3bjA9thxh4GHjNpHoYDiQ24tJx/Y2aQCNbC/60g4d9h+x4G/m/4tdzIMX5wEGILG8NHIANoCxteLfY33pgxNralGQP9Yiw5sy89uecwm7HlHINknFok+3OMP/5ss5Hd2H/44Weeb9a27e3ND2+8qbDDqYUBFh2GcBXMIMIAt3qQkg8gUh6vmlEwCkbBKBjRAACs7VhzSGKrfwAAAABJRU5ErkJggg==","orcid":"","institution":"Xi'an University of Architecture and Technology","correspondingAuthor":true,"prefix":"","firstName":"Zhoujie","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-01-12 03:09:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3855462/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3855462/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91773965,"identity":"32883abf-ebd6-46fe-8630-260940bc101f","added_by":"auto","created_at":"2025-09-20 17:28:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":487821,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3855462/v1_covered_1fc89f15-560f-4f23-be5a-2aee77e235da.pdf"}],"financialInterests":"","formattedTitle":"Exploring the Hydraulic Properties of Unsaturated Soil Using Deep Learning and Digital Imaging Measurement","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":"Deep Learning, Neural network, Digital imaging measurement, Soil-Water Characteristic Curve, Physical parameters","lastPublishedDoi":"10.21203/rs.3.rs-3855462/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3855462/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aim of this work is to improve the accuracy of the traditional Unsaturated Soil Hydraulic Property (USHP) analysis model. To this end, this work combines digital imaging measurement and Deep Learning technology to analyze the USHP. Firstly, the research status of basic characteristics of unsaturated soil and the application status of Deep Learning is analyzed. The second step examines the impact of soil specimens\u0026rsquo; physical properties on their hydraulic properties. This includes acquiring hydraulic parameters and the Soil-Water Characteristic Curve (SWCC) based on a full-surface digital imaging measurement. Finally, an SHP model based on the Backpropagation Neural Network (BPNN) is implemented, trained, and validated. The results show that the proposed model\u0026rsquo;s predicted SWCC is consistent with the experimental phenomena of previous studies. Also, the proposed USHP BPNN model uses the Levenberg Marquardt (LM) algorithm to achieve the best results, with shorter computational time and less noise than other algorithms. Lastly, the results of the proposed model\u0026rsquo;s various parameters help determine that the optimal number of neurons in the hidden layer is ten. The proposed model\u0026rsquo;s error is more minor than several literature models thanks to considering the correlation between physical parameters like soil particle size and SHP. Overall, the BPNN is used to model the soil\u0026rsquo;s physical and hydraulic parameters and has shown excellent performance. 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