A strain reconstruction method for uncertain boundary conditions

A strain reconstruction method for uncertain boundary conditions | 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 strain reconstruction method for uncertain boundary conditions junzhou Huo, Lihan Ge, zhange Zhang, zejiang Wang, Xin Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2703435/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 Affected by complex boundary conditions and redundant structural assembly, it is difficult to collect stress and strain information on key structures of complex mechanical equipment, resulting in difficult life monitoring and large calculation errors. The purpose of this study is to provide reliable strain data for accurate life prediction and solve the problem of obtaining critical structural strain under uncertain loads and other complex boundary conditions. In this paper, a neural network training dataset is established based on finite element analysis data and experimental data, and the neural network architecture and parameter optimization are carried out for each data set. Finally, the strain reconstruction model based on a neural network is established, which solves the two problems of accurate equivalence from structure to strain and equivalence from strain to load. Feature experimental samples were designed to verify the universality of the research method. The model is experimentally verified with an average error of less than 5%. It realizes the global strain reconstruction from the local measurement point to the overall structure, which can be used for real-time and full-cycle life monitoring of the structure; feature structure multi-layer neural networks uncertain boundary conditions Strain reconstruction 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. 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-2703435","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":200477124,"identity":"5bf7d66a-711d-4d27-a7b5-c511977af578","order_by":0,"name":"junzhou Huo","email":"","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"junzhou","middleName":"","lastName":"Huo","suffix":""},{"id":200477125,"identity":"207d192b-c2fe-4fa9-ab13-da8356bf9435","order_by":1,"name":"Lihan Ge","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBAC/gbmhsNAWs6+vf/jg4SKGsJaJA4wgrUYG/AcMDZ4cOYYYS0GDIwNzEA6cYNEgpnkwxZmIrSwNzYeLqi5k7idISGtIrGBjYG/vTsBvxaegw2HZxx7Zryz4cCxG4k7ZBgkzpzdgFeL4Y3EhsM8bIdlGw42tt1IPMPGYCCRi1+Lwf2HQC3/DgMDgZmtILGNmQgtN4CKedsOK244xsbGQJQWiTNAh/H2HTaW7OFhlkg4c4yHoF/42w8f/szz7bAcv/wbxo8/Kmrk+Nt78WvBADykKR8Fo2AUjIJRgBUAAPdgUwqukFFVAAAAAElFTkSuQmCC","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lihan","middleName":"","lastName":"Ge","suffix":""},{"id":200477126,"identity":"2c9a732f-1a1a-4224-b73b-684265a87cd3","order_by":2,"name":"zhange Zhang","email":"","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"zhange","middleName":"","lastName":"Zhang","suffix":""},{"id":200477127,"identity":"d7c0cff3-ee58-4140-8917-05ee495e6d35","order_by":3,"name":"zejiang Wang","email":"","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"zejiang","middleName":"","lastName":"Wang","suffix":""},{"id":200477128,"identity":"816d5b52-9686-41c3-b7ad-4c35c0d738b2","order_by":4,"name":"Xin Zhao","email":"","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2023-03-17 07:11:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2703435/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2703435/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":38256835,"identity":"5aebd78e-14fc-4b1b-b8cc-09e9b23312f6","added_by":"auto","created_at":"2023-06-09 02:03:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1017693,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2703435/v1_covered_1ccfb632-7892-4782-904b-6eee0889478f.pdf"}],"financialInterests":"","formattedTitle":"A strain reconstruction method for uncertain boundary conditions","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":"feature structure, multi-layer neural networks, uncertain boundary conditions, Strain reconstruction","lastPublishedDoi":"10.21203/rs.3.rs-2703435/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2703435/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Affected by complex boundary conditions and redundant structural assembly, it is difficult to collect stress and strain information on key structures of complex mechanical equipment, resulting in difficult life monitoring and large calculation errors. The purpose of this study is to provide reliable strain data for accurate life prediction and solve the problem of obtaining critical structural strain under uncertain loads and other complex boundary conditions. In this paper, a neural network training dataset is established based on finite element analysis data and experimental data, and the neural network architecture and parameter optimization are carried out for each data set. Finally, the strain reconstruction model based on a neural network is established, which solves the two problems of accurate equivalence from structure to strain and equivalence from strain to load. Feature experimental samples were designed to verify the universality of the research method. The model is experimentally verified with an average error of less than 5%. 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