Probabilistic analysis of pile foundations using Monte Carlo and Subset simulations compared with FOSM-based hybrid ANN paradigm

Probabilistic analysis of pile foundations using Monte Carlo and Subset simulations compared with FOSM-based hybrid ANN paradigm | 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 Probabilistic analysis of pile foundations using Monte Carlo and Subset simulations compared with FOSM-based hybrid ANN paradigm Subodh Kumar Suman, Avijit Burman, Shiva Shankar Choudhary This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4471833/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 presence of uncertainty and variability in soil and sub-soils is a fundamental aspect of pile design. Thus, extensive study has been conducted to accurately measure the reliability or likelihood of structural failure. This work investigates the suitability of Monte Carlo and Subset simulations for the risk assessment of piles. In addition, First-order second-moment method (FOSM)-based hybrid artificial neural network (ANN) frameworks were used. Specifically, five hybrid ANNs were constructed using swarm intelligence algorithms A comparative analysis of Monte Carlo, Subset, FOSM, and FOSM-based hybrid ANN was conducted at different co-efficient of variation levels. The effectiveness of the utilized hybrid ANNs was determined using diverse statistical indices. Based on the performance, the best-fitted hybrid ANN was selected and utilized for risk assessment of pile foundations. According to the results, the employed ANN-MPA framework exhibit the best-fitted estimation with 99.2% (R2 = 0.9920) accuracy. The probability of failure was subsequently determined using Monte Carlo, Subset simulation and FOSM-based ANN-MPA methods. According to the results, the FOSM-based ANN-MPA approach can be considered as an alternative tool for quick estimation of risk assessment of pile foundations under different coefficient of variations levels. Reliability analysis Pile foundation design First-order second-moment method Artificial neural network Soft computing 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. 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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-4471833","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309544252,"identity":"419155bd-837e-46c7-b8ea-7342150214ed","order_by":0,"name":"Subodh Kumar Suman","email":"data:image/png;base64,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","orcid":"","institution":"National Institute of Technology Patna","correspondingAuthor":true,"prefix":"","firstName":"Subodh","middleName":"Kumar","lastName":"Suman","suffix":""},{"id":309544254,"identity":"dd6df8db-a64e-4883-81cc-de329a3c00c2","order_by":1,"name":"Avijit Burman","email":"","orcid":"","institution":"National Institute of Technology Patna","correspondingAuthor":false,"prefix":"","firstName":"Avijit","middleName":"","lastName":"Burman","suffix":""},{"id":309544256,"identity":"29d7516f-6311-4255-8414-ec49e8b78fb8","order_by":2,"name":"Shiva Shankar Choudhary","email":"","orcid":"","institution":"National Institute of Technology Patna","correspondingAuthor":false,"prefix":"","firstName":"Shiva","middleName":"Shankar","lastName":"Choudhary","suffix":""}],"badges":[],"createdAt":"2024-05-24 10:25:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4471833/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4471833/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65051201,"identity":"0de53600-9f17-4489-a3ea-f66d7936492f","added_by":"auto","created_at":"2024-09-23 06:05:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2346121,"visible":true,"origin":"","legend":"","description":"","filename":"S2.MainManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4471833/v1_covered_3afaacb9-599c-4abd-a6cd-f5c7d24f5c99.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Probabilistic analysis of pile foundations using Monte Carlo and Subset simulations compared with FOSM-based hybrid ANN paradigm","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":"Reliability analysis, Pile foundation design, First-order second-moment method, Artificial neural network, Soft computing","lastPublishedDoi":"10.21203/rs.3.rs-4471833/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4471833/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The presence of uncertainty and variability in soil and sub-soils is a fundamental aspect of pile design. 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