Beyond Conventions: Evaluating Ensemble Learning Methods for Precise Fatigue Life Estimation

Beyond Conventions: Evaluating Ensemble Learning Methods for Precise Fatigue Life Estimation | 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 Beyond Conventions: Evaluating Ensemble Learning Methods for Precise Fatigue Life Estimation Sasan Farhadi, Samuele Tatullo, Francesco Ferrian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4698107/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Efficient prediction of fatigue life in structural components is crucial for ensur- ing their integrity and reliability, especially considering the dominant occurrence of fatigue failure in metallic structures within the industrial sectors. Conven- tional fatigue assessment methods, although theoretically established, are often time-consuming and exhibit limitations due to the intricate nature of the fatigue mechanism. Machine learning models have demonstrated significant potential for enhancing the efficiency of predictions in fatigue life. This research explores the effectiveness of ensemble learning models—boosting, stacking, and bag- ging—compared to linear regression and K-Nearest Neighbors as benchmarks. Fatigue life prediction is conducted across different notched scenarios using Incre- mental Energy Release Rate (IERR) measures in addition to the more standard stress/strain field measures. To assess the performance of the proposed models, a comprehensive set of evaluation metrics was performed, including mean square error (MSE), mean squared logarithmic error (MSLE), symmetric mean abso- lute percentage (SMAPE), and Tweedie score. The findings reveal that ensemble learning models, particularly the ensemble neural networks, stands out as a superior approach for fatigue life cycle assessment compared to other methods. Moreover, the integration of IERR in predicting fatigue life for notched-shape components indicates a promising approach for enhancing the reliability and efficiency of fatigue life predictions in real-world industrial applications. fatigue life cycle notched components machine learning ensemble learning incremental energy release rate Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Aug, 2024 Reviews received at journal 03 Aug, 2024 Reviews received at journal 31 Jul, 2024 Reviewers agreed at journal 17 Jul, 2024 Reviewers agreed at journal 16 Jul, 2024 Reviewers invited by journal 16 Jul, 2024 Editor assigned by journal 16 Jul, 2024 Editor invited by journal 11 Jul, 2024 Submission checks completed at journal 09 Jul, 2024 First submitted to journal 06 Jul, 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. 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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-4698107","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":334277115,"identity":"da23bee9-28ba-448e-9293-45b882eb5370","order_by":0,"name":"Sasan Farhadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYPACOQYGCSBKqACy2RkYGBsIazGGajnDwMDDTJIWxjYitMi3H3/4uYDBQN58dvPDGw/n2STuZ2Y++HAGg52cLg59BmdyjKVnMBgYzrlzzNgicVtaYg8zW7LhBoZkY7MDOLQw5DBI8zD8YZwhkWAmkbjtMFALj5nkA4YDidtwaJHvf/74Nw+Dgf0MifRvEolziNDCcCPBDGiLQeIMiRygLQ1QLRvwaDG48cbMGqglGail2CLhWJpxz2GgX2YY4PaLfH/649tALbZAh228+aPGRra9vfngw54KOzlcWsCA8R+WYBkFo2AUjIJRQAEAAFLtVmPelfiTAAAAAElFTkSuQmCC","orcid":"","institution":"Polytechnic University of Turin","correspondingAuthor":true,"prefix":"","firstName":"Sasan","middleName":"","lastName":"Farhadi","suffix":""},{"id":334277117,"identity":"0c2e7d04-ed22-445d-a76a-161cdf995bbd","order_by":1,"name":"Samuele Tatullo","email":"","orcid":"","institution":"IMT School for Advanced Studies Lucca","correspondingAuthor":false,"prefix":"","firstName":"Samuele","middleName":"","lastName":"Tatullo","suffix":""},{"id":334277118,"identity":"98602212-995d-43ca-b648-f8dcde59ebbc","order_by":2,"name":"Francesco Ferrian","email":"","orcid":"","institution":"Polytechnic University of Turin","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Ferrian","suffix":""}],"badges":[],"createdAt":"2024-07-06 20:26:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4698107/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4698107/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-79476-y","type":"published","date":"2025-04-01T15:56:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80081902,"identity":"ea8924f3-52f7-42be-afcb-bff331cb5d0e","added_by":"auto","created_at":"2025-04-07 16:00:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2394237,"visible":true,"origin":"","legend":"","description":"","filename":"farhadietalmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4698107/v1_covered_762fa460-4190-4a5c-a1d3-073e891c1b1a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond Conventions: Evaluating Ensemble Learning Methods for Precise Fatigue Life Estimation","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":"fatigue life cycle, notched components, machine learning, ensemble learning, incremental energy release rate","lastPublishedDoi":"10.21203/rs.3.rs-4698107/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4698107/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Efficient prediction of fatigue life in structural components is crucial for ensur- ing their integrity and reliability, especially considering the dominant occurrence of fatigue failure in metallic structures within the industrial sectors. Conven- tional fatigue assessment methods, although theoretically established, are often time-consuming and exhibit limitations due to the intricate nature of the fatigue mechanism. Machine learning models have demonstrated significant potential for enhancing the efficiency of predictions in fatigue life. This research explores the effectiveness of ensemble learning models—boosting, stacking, and bag- ging—compared to linear regression and K-Nearest Neighbors as benchmarks. Fatigue life prediction is conducted across different notched scenarios using Incre- mental Energy Release Rate (IERR) measures in addition to the more standard stress/strain field measures. To assess the performance of the proposed models, a comprehensive set of evaluation metrics was performed, including mean square error (MSE), mean squared logarithmic error (MSLE), symmetric mean abso- lute percentage (SMAPE), and Tweedie score. The findings reveal that ensemble learning models, particularly the ensemble neural networks, stands out as a superior approach for fatigue life cycle assessment compared to other methods. 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