Fuzzy random sensitivity analysis for the overall structure reliability of reinforced concrete freezing wellbores in deep alluvium based on HMM model

Fuzzy random sensitivity analysis for the overall structure reliability of reinforced concrete freezing wellbores in deep alluvium based on HMM model | 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 Article Fuzzy random sensitivity analysis for the overall structure reliability of reinforced concrete freezing wellbores in deep alluvium based on HMM model YAO Yafeng, ZHU Yan, LI Yongheng, WANG Wei, ZHANG Zhemei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4225490/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract To address the shortcomings of traditional reliability theory in characterizing the stability of deep underground structures, the advanced first order second moment of reliability was improved to obtain fuzzy random reliability, which is more consistent with the working conditions. The traditional sensitivity analysis model was optimized using fuzzy random optimization, and an analytical calculation model of the mean and standard deviation of the fuzzy random reliability sensitivity was established. A big data hidden Markov model and expectation-maximization algorithm were used to improve the digital characteristics of fuzzy random variables. The fuzzy random sensitivity optimization model was used to confirm the effect of concrete compressive strength, thick-diameter ratio, reinforcement ratio, uncertainty coefficient of calculation model, and soil depth on the overall structural reliability of a reinforced concrete double-layer wellbore in deep alluvial soil. Through numerical calculations, these characteristics were observed to be the main influencing factors. Furthermore, while the soil depth was negatively correlated, the other influencing factors were all positively correlated with the overall reliability. This study provides an effective reference for the safe construction of deep underground structures in the future. Physical sciences/Mathematics and computing/Applied mathematics Physical sciences/Mathematics and computing/Computer science Physical sciences/Engineering/Civil engineering sensitivity analysis fuzzy random reliability overall structure big data hidden Markov model mode Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 May, 2024 Reviews received at journal 21 May, 2024 Reviews received at journal 18 May, 2024 Reviewers agreed at journal 02 May, 2024 Reviewers agreed at journal 02 May, 2024 Reviewers invited by journal 02 May, 2024 Editor assigned by journal 02 May, 2024 Editor invited by journal 13 Apr, 2024 Submission checks completed at journal 13 Apr, 2024 First submitted to journal 05 Apr, 2024 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-4225490","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":292014563,"identity":"5b8f10b9-3de1-4d6a-abb7-80dd4fe573a6","order_by":0,"name":"YAO Yafeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDACZjYwxc8PYpOkRXJmA1QLD2E9UC0bDhCrRbedLfEBY9thCeMbyQc/FzDckbMnpMXsMNthA5AWsxtpydIzGJ4ZE7TF7DB7mwRQS53ZjRwDaR6Gw4k9RGhp/wF22Iz8z7+BWuqJ0MJ2jAGkxUAihw1kSwIRDmNLlkg4ly4hceaZmTWPwWHDngOEtJw/ZvjhQ5m1BH978uPbPBWH5dkbCFkDAolszQwMAglAlgExysHgTx0wyRBy0CgYBaNgFIxYAAAB3zw5e7+JYAAAAABJRU5ErkJggg==","orcid":"","institution":"Nantong Vocational University","correspondingAuthor":true,"prefix":"","firstName":"YAO","middleName":"","lastName":"Yafeng","suffix":""},{"id":292014564,"identity":"431ec711-a2df-4fef-a90c-967a2d22e26a","order_by":1,"name":"ZHU Yan","email":"","orcid":"","institution":"Nantong Vocational University","correspondingAuthor":false,"prefix":"","firstName":"ZHU","middleName":"","lastName":"Yan","suffix":""},{"id":292014565,"identity":"130ac1b9-5cbd-4030-aeac-47e7befa9226","order_by":2,"name":"LI Yongheng","email":"","orcid":"","institution":"Anhui Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"LI","middleName":"","lastName":"Yongheng","suffix":""},{"id":292014566,"identity":"c2edb138-0718-4f0f-9489-edb8ec852019","order_by":3,"name":"WANG Wei","email":"","orcid":"","institution":"Nantong Vocational University","correspondingAuthor":false,"prefix":"","firstName":"WANG","middleName":"","lastName":"Wei","suffix":""},{"id":292014567,"identity":"4a1195ec-d2e2-41f3-9462-370a1c962800","order_by":4,"name":"ZHANG Zhemei","email":"","orcid":"","institution":"City University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"ZHANG","middleName":"","lastName":"Zhemei","suffix":""}],"badges":[],"createdAt":"2024-04-06 03:30:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4225490/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4225490/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54813763,"identity":"269f010e-a595-433d-851c-0538dde02f37","added_by":"auto","created_at":"2024-04-17 06:50:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":509949,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4225490/v1_covered_09992bae-a056-4fc7-97ff-7e8f74fd10ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fuzzy random sensitivity analysis for the overall structure reliability of reinforced concrete freezing wellbores in deep alluvium based on HMM model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sensitivity analysis, fuzzy random reliability, overall structure, big data, hidden Markov model mode","lastPublishedDoi":"10.21203/rs.3.rs-4225490/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4225490/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo address the shortcomings of traditional reliability theory in characterizing the stability of deep underground structures, the advanced first order second moment of reliability was improved to obtain fuzzy random reliability, which is more consistent with the working conditions. 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