A First-Order Neural Network-Driven Method for Probabilistic Slope Stability Analysis

A First-Order Neural Network-Driven Method for Probabilistic Slope Stability Analysis | 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 A First-Order Neural Network-Driven Method for Probabilistic Slope Stability Analysis Parnia Karimi, Amir Gholampour This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6738220/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 In this study, a practical framework is developed for intelligent probabilistic analysis and prediction of soil slope stability. A MATLAB-based program is coded to perform finite element slope stability simulations and generate synthetic datasets. These datasets are subsequently employed in an artificial neural network to identify the linear limit state function. The first-order second-moment method is then applied to perform the probabilistic analysis. Numerous numerical tests are carried out to identify the optimized deep network through Monte Carlo concepts. It is found that the network consisting of 5 hidden layers with 19, 5, 6, 57, and 64 neurons is the optimum solution in the context of the studied engineering problem. The proposed methodology is validated through two case studies. Results indicate that the neural network model is capable of predicting the mean of the safety factor with an average difference of less than 4.3% compared to conventional approved methods based on random variables and random fields. Furthermore, the findings suggest that the proposed model can rapidly and accurately estimate the performance level of a design, whereas the safety factor alone may not serve as a reliable indicator of the actual slope conditions. Soil slope stability Probabilistic analysis Artificial neural network (ANN) First-Order Second-Moment (FOSM) Roadside slope 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-6738220","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463697284,"identity":"d99125c5-52e3-47f8-b4da-3f2ea4f4bdde","order_by":0,"name":"Parnia Karimi","email":"","orcid":"","institution":"Apadana Institute of Higher Education","correspondingAuthor":false,"prefix":"","firstName":"Parnia","middleName":"","lastName":"Karimi","suffix":""},{"id":463697285,"identity":"6ba5b213-0c45-4d65-b1c9-8242ca7520ae","order_by":1,"name":"Amir Gholampour","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABGElEQVRIiWNgGAWjYHACNgaGAigzocJGjh/MKMCjAazFAMp8cCbNWLIBpMUAjwZkLYwP2w4nbjgAYuLRwt/Afu3BBwO7aHP2HrMPiW1pxsbnVyd+eGDAIM8vdgCrFokDPOWGMwySc3f2nDGekXDORs7sxtvNEkCHGc6cnYDdmgM8adI8Bsy5G27kGDMklKUZm904uwGkJcHgNnYt8hAt9VAtbIcTN884u/kHPi0GB9iPAbUchmoBeZ+/dxteWwwP87BJzjA4DvTLsWKGBGAgS9zg3WaRYCCB0y9yx9ufSXyoqM7dzt68mfEHKCr7z26+CWTI80vj8D4zDyQKEBEhAVYpgV05GLA/QNPCfwCP6lEwCkbBKBiJAACSV2Lykxu9YgAAAABJRU5ErkJggg==","orcid":"","institution":"Apadana Institute of Higher Education","correspondingAuthor":true,"prefix":"","firstName":"Amir","middleName":"","lastName":"Gholampour","suffix":""}],"badges":[],"createdAt":"2025-05-24 09:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6738220/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6738220/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84630751,"identity":"5f85cbef-5499-4c43-be6a-f45ee48effa5","added_by":"auto","created_at":"2025-06-15 09:16:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1078021,"visible":true,"origin":"","legend":"","description":"","filename":"slopeANNpaper2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6738220/v1_covered_9a9825d8-dc92-4a70-8e74-aae694fa3e5a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A First-Order Neural Network-Driven Method for Probabilistic Slope Stability Analysis","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":"Soil slope stability, Probabilistic analysis, Artificial neural network (ANN), First-Order Second-Moment (FOSM), Roadside slope","lastPublishedDoi":"10.21203/rs.3.rs-6738220/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6738220/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, a practical framework is developed for intelligent probabilistic analysis and prediction of soil slope stability. A MATLAB-based program is coded to perform finite element slope stability simulations and generate synthetic datasets. These datasets are subsequently employed in an artificial neural network to identify the linear limit state function. The first-order second-moment method is then applied to perform the probabilistic analysis. Numerous numerical tests are carried out to identify the optimized deep network through Monte Carlo concepts. It is found that the network consisting of 5 hidden layers with 19, 5, 6, 57, and 64 neurons is the optimum solution in the context of the studied engineering problem. The proposed methodology is validated through two case studies. Results indicate that the neural network model is capable of predicting the mean of the safety factor with an average difference of less than 4.3% compared to conventional approved methods based on random variables and random fields. Furthermore, the findings suggest that the proposed model can rapidly and accurately estimate the performance level of a design, whereas the safety factor alone may not serve as a reliable indicator of the actual slope conditions.\u003c/p\u003e","manuscriptTitle":"A First-Order Neural Network-Driven Method for Probabilistic Slope Stability Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 17:02:00","doi":"10.21203/rs.3.rs-6738220/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":"d5f9cd53-69d0-468e-8eba-f1b4868d9312","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-15T09:08:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-30 17:02:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6738220","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6738220","identity":"rs-6738220","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}