Adaptive Kriging-based method with learning function allocation scheme and hybrid convergence criterion for efficient structural reliability analysis

Adaptive Kriging-based method with learning function allocation scheme and hybrid convergence criterion for efficient structural reliability analysis | 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 Adaptive Kriging-based method with learning function allocation scheme and hybrid convergence criterion for efficient structural reliability analysis Jiaguo Zhou, Guoji Xu, Zexing Jiang, Yongle Li, Jinsheng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3940108/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Sep, 2024 Read the published version in Engineering with Computers → Version 1 posted 7 You are reading this latest preprint version Abstract Structural reliability analysis poses a considerable challenge in engineering practice, leading to the development of various state-of-the-art methods. Active learning methods, known for their superior performance, have been extensively investigated for assessing failure probabilities in structural reliability analysis. This paper aims to develop an efficient and accurate Kriging-based active learning method for structural reliability analysis, incorporating a novel learning function allocation scheme and a hybrid convergence criterion. Specifically, a novel learning function allocation scheme is introduced to address the challenge of no single learning function demonstrating superior performance across various engineering contexts. Six active learning functions, including EFF, H, REIF, LIF, FNEIF, and KO, are used to form a portfolio of functions in the allocation scheme. The use of the U learning function initiates the active learning process with a truncated candidate sample pool, alleviating computational burden while maintaining accuracy. Additionally, a hybrid convergence criterion, combining the error-based stopping criterion with a stabilization convergence criterion, is proposed to terminate the active learning process at an appropriate stage. The performance of the proposed method is evaluated through four numerical examples and one engineering case, demonstrating its accuracy and efficiency. Structural reliability analysis learning function allocation scheme active learning hybrid convergence criterion Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Sep, 2024 Read the published version in Engineering with Computers → Version 1 posted Editorial decision: Revision requested 02 May, 2024 Reviews received at journal 02 May, 2024 Reviewers agreed at journal 03 Apr, 2024 Reviewers invited by journal 03 Apr, 2024 Editor assigned by journal 11 Feb, 2024 Submission checks completed at journal 09 Feb, 2024 First submitted to journal 08 Feb, 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. 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-3940108","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272345754,"identity":"0de5d8c0-3b52-441e-a5bd-23b7c7610ac6","order_by":0,"name":"Jiaguo Zhou","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jiaguo","middleName":"","lastName":"Zhou","suffix":""},{"id":272345755,"identity":"9236e342-13bb-4a37-a83e-24c55f232d8e","order_by":1,"name":"Guoji Xu","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Guoji","middleName":"","lastName":"Xu","suffix":""},{"id":272345756,"identity":"d4bbc6e1-9ec2-4cf6-9644-1f388bad9ea8","order_by":2,"name":"Zexing Jiang","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Zexing","middleName":"","lastName":"Jiang","suffix":""},{"id":272345757,"identity":"52cfe04f-8e4e-4240-b831-980d404373dd","order_by":3,"name":"Yongle Li","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yongle","middleName":"","lastName":"Li","suffix":""},{"id":272345758,"identity":"1744c62a-a623-403b-9c97-fa7e9b381b57","order_by":4,"name":"Jinsheng Wang","email":"data:image/png;base64,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","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Jinsheng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-02-08 13:51:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3940108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3940108/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00366-024-02044-5","type":"published","date":"2024-09-15T15:58:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64620209,"identity":"0878e02b-bbcb-4847-8722-bae6a1f24074","added_by":"auto","created_at":"2024-09-16 16:18:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1271846,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3940108/v1_covered_df7ba8c0-efc9-42b6-a770-dd0a4658a5b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Kriging-based method with learning function allocation scheme and hybrid convergence criterion for efficient structural reliability analysis","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":"engineering-with-computers","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ewco","sideBox":"Learn more about [Engineering with Computers](http://link.springer.com/journal/366)","snPcode":"366","submissionUrl":"https://submission.nature.com/new-submission/366/3","title":"Engineering with Computers","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Structural reliability analysis, learning function allocation scheme, active learning, hybrid convergence criterion","lastPublishedDoi":"10.21203/rs.3.rs-3940108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3940108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Structural reliability analysis poses a considerable challenge in engineering practice, leading to the development of various state-of-the-art methods. Active learning methods, known for their superior performance, have been extensively investigated for assessing failure probabilities in structural reliability analysis. This paper aims to develop an efficient and accurate Kriging-based active learning method for structural reliability analysis, incorporating a novel learning function allocation scheme and a hybrid convergence criterion. Specifically, a novel learning function allocation scheme is introduced to address the challenge of no single learning function demonstrating superior performance across various engineering contexts. Six active learning functions, including EFF, H, REIF, LIF, FNEIF, and KO, are used to form a portfolio of functions in the allocation scheme. The use of the U learning function initiates the active learning process with a truncated candidate sample pool, alleviating computational burden while maintaining accuracy. Additionally, a hybrid convergence criterion, combining the error-based stopping criterion with a stabilization convergence criterion, is proposed to terminate the active learning process at an appropriate stage. The performance of the proposed method is evaluated through four numerical examples and one engineering case, demonstrating its accuracy and efficiency.","manuscriptTitle":"Adaptive Kriging-based method with learning function allocation scheme and hybrid convergence criterion for efficient structural reliability analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-13 06:40:56","doi":"10.21203/rs.3.rs-3940108/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-03T03:39:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-02T23:00:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57039fcd-9d6c-434c-a68d-f9a8acbdef27","date":"2024-04-03T17:20:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-03T04:36:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-12T01:38:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-09T09:57:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Engineering with Computers","date":"2024-02-08T13:40:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"engineering-with-computers","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ewco","sideBox":"Learn more about [Engineering with Computers](http://link.springer.com/journal/366)","snPcode":"366","submissionUrl":"https://submission.nature.com/new-submission/366/3","title":"Engineering with Computers","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"11028d3b-40da-47d1-975c-969956d95681","owner":[],"postedDate":"February 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T16:14:25+00:00","versionOfRecord":{"articleIdentity":"rs-3940108","link":"https://doi.org/10.1007/s00366-024-02044-5","journal":{"identity":"engineering-with-computers","isVorOnly":false,"title":"Engineering with Computers"},"publishedOn":"2024-09-15 15:58:26","publishedOnDateReadable":"September 15th, 2024"},"versionCreatedAt":"2024-02-13 06:40:56","video":"","vorDoi":"10.1007/s00366-024-02044-5","vorDoiUrl":"https://doi.org/10.1007/s00366-024-02044-5","workflowStages":[]},"version":"v1","identity":"rs-3940108","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3940108","identity":"rs-3940108","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}