Prediction of rubber fatigue life using an assimilation-based learning approach and incremental crack propagation model

Prediction of rubber fatigue life using an assimilation-based learning approach and incremental crack propagation 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 Research Article Prediction of rubber fatigue life using an assimilation-based learning approach and incremental crack propagation model Congzhuo Fang, Yanfu Chen, Zihao Yang, Yiyuan Zhang, Xindang He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3961128/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 Accurately and efficiently predicting the fatigue life of rubber materials has beena long-standing challenge due to limited understanding of the fatigue mechanism.In this study, a variational assimilation-based machine learning method assistedwith incremental crack propagation model is proposed to predict the fatigue lifeof rubber materials. Firstly, according to the fracture mechanics theory, a newrubber fatigue life prediction model based on incremental crack propagation andsparse experimental data is established, which owns higher accuracy than theclassical crack energy density model. Further, a rubber fatigue life solver coupled incremental crack propagation model and nonlinear finite element method isintroduced to generate a dense fatigue life dataset of rubber materials with highaccuracy. Finally, the artificial neural network model is trained, cross-validatedand tested using the dense dataset, and the three-dimensional variational assimilation model is employed to merge the predicted values of artificial neural networkwith experimental data. By comparing against the experimental data, the effectiveness of the proposed method was verified, thereby we offer an accurate andefficient approach to predict the rubber fatigue life. Fatigue life incremental crack propagation model neural network variational assimilation rubber materials 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-3961128","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273348064,"identity":"15efa6f6-bdd2-48c2-9d3d-3bd7b8c5193f","order_by":0,"name":"Congzhuo Fang","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Congzhuo","middleName":"","lastName":"Fang","suffix":""},{"id":273348065,"identity":"bd991930-6e3f-4a78-b5c2-e1850aab2996","order_by":1,"name":"Yanfu Chen","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Yanfu","middleName":"","lastName":"Chen","suffix":""},{"id":273348066,"identity":"ae309769-a616-411e-99a6-3df48d1bfc2e","order_by":2,"name":"Zihao Yang","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Zihao","middleName":"","lastName":"Yang","suffix":""},{"id":273348070,"identity":"364289bd-a6ec-40c8-a4f0-25570919e271","order_by":3,"name":"Yiyuan Zhang","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Yiyuan","middleName":"","lastName":"Zhang","suffix":""},{"id":273348071,"identity":"13d55621-fbdd-4493-a7ad-8b337ea02418","order_by":4,"name":"Xindang He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYBACAwYGwwcINpFajA0QbCK1mEmQpsVcInlbdWHbncQG9uZtEgw1dwhrsew5VnZ7ZtuzxAaeY2USDMeeEeGw4z1mt3m3HU5skMgxk2BsOEyElsM8ZsVgLfJviNUCtIUZYgsPsVrOHCuW5v33zLiNJ63YIuEYMVpuJG/8zHPmjmw/++GNNz7UEKEFCg4wsIGoBKI1gLSMglEwCkbBKMAJAM3eOmVKgK4qAAAAAElFTkSuQmCC","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":true,"prefix":"","firstName":"Xindang","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2024-02-16 11:54:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3961128/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3961128/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51405025,"identity":"5d3c5c6d-c9d9-4726-9a17-b99fa420378f","added_by":"auto","created_at":"2024-02-21 02:25:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":595012,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3961128/v1_covered_30103cb5-f89a-4fd2-ac34-ff416dff82e4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of rubber fatigue life using an assimilation-based learning approach and incremental crack propagation model","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":"Fatigue life, incremental crack propagation model, neural network, variational assimilation, rubber materials","lastPublishedDoi":"10.21203/rs.3.rs-3961128/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3961128/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":" Accurately and efficiently predicting the fatigue life of rubber materials has beena long-standing challenge due to limited understanding of the fatigue mechanism.In this study, a variational assimilation-based machine learning method assistedwith incremental crack propagation model is proposed to predict the fatigue lifeof rubber materials. Firstly, according to the fracture mechanics theory, a newrubber fatigue life prediction model based on incremental crack propagation andsparse experimental data is established, which owns higher accuracy than theclassical crack energy density model. Further, a rubber fatigue life solver coupled incremental crack propagation model and nonlinear finite element method isintroduced to generate a dense fatigue life dataset of rubber materials with highaccuracy. Finally, the artificial neural network model is trained, cross-validatedand tested using the dense dataset, and the three-dimensional variational assimilation model is employed to merge the predicted values of artificial neural networkwith experimental data. 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