Efficient system reliability analysis of landslide-pipeline interaction via active learning Kriging with sequential importance sampling

Efficient system reliability analysis of landslide-pipeline interaction via active learning Kriging with sequential importance sampling | 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 Efficient system reliability analysis of landslide-pipeline interaction via active learning Kriging with sequential importance sampling Liu Yang, Jie Yang, Xudong Qu, Lin Cheng, Chunhui Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9359121/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract System reliability assessment of buried pipelines subjected to landslide hazards is computationally challenging due to high-dimensional spatial variability and complex nonlinear large-deformation responses. This paper proposes AK-SYS-SIS, integrating Sequential Importance Sampling (SIS) into the Active Learning Kriging framework for system reliability analysis. The method constructs an approximate optimal sampling density to adaptively guide candidates toward critical boundaries of multiple failure modes, while a composite learning strategy selectively updates only the most critical surrogate component. Two stochastic large-deformation finite element frameworks, RCEL-OLHS and GCRCEL-OLHS, are developed within the Coupled Eulerian-Lagrangian (CEL) technique to simulate earthquake-induced and rainfall-induced landslide–pipeline interactions, respectively. These frameworks incorporate Karhunen–Loève random field discretization, strain-softening constitutive models via Abaqus VUSDFLD/UMAT subroutines, modified Mohr-Coulomb plasticity, and soil rotating anisotropy, with GPU-accelerated random field generation for the rainfall scenario. Benchmark validations on series, parallel, and hybrid systems demonstrate that AK-SYS-SIS improves efficiency by 4–6 orders of magnitude over Monte Carlo Simulation with relative errors below 3%. In engineering applications, the method identifies system failure probabilities with fewer than 100 finite element evaluations, reducing computational cost by 94%–96.5% compared to direct simulation. System reliability analysis Active learning Kriging Sequential importance sampling Coupled Eulerian-Lagrangian (CEL) Landslide-pipeline interaction Spatial variability Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 28 Apr, 2026 Reviews received at journal 26 Apr, 2026 Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor assigned by journal 10 Apr, 2026 Submission checks completed at journal 08 Apr, 2026 First submitted to journal 08 Apr, 2026 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-9359121","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625549457,"identity":"3d603ce1-ab50-4483-a152-239d6f108eab","order_by":0,"name":"Liu Yang","email":"","orcid":"","institution":"Xi’an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Yang","suffix":""},{"id":625549461,"identity":"cac81ba7-3786-4e83-bb7b-25ce73c2bd76","order_by":1,"name":"Jie Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYFCCAyDChoefv4EkLQlpMpIzDpBkU8JhG4OGBCIVGxw8Y/iZ98d5HgOGA4wfPuYQo+XAsWRpnoTbPObMDcySM7cRpeXwAbAWy4YDbMy8xGk52PybJ+Ecj8GBBKK1HD4GtOUACVokDxxLs5yTlswjOeNgM3F+4btxxvjGGxs7e37+5oMfPhKjReHGARiTsYEI9UAg30+kwlEwCkbBKBjBAAAgtDpBF1ud8QAAAABJRU5ErkJggg==","orcid":"","institution":"Xi’an University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Yang","suffix":""},{"id":625549465,"identity":"0e7a27e6-740c-41c9-8b02-e555b381416a","order_by":2,"name":"Xudong Qu","email":"","orcid":"","institution":"Sinohydro Engineering Bureau 3 CO.,LTD","correspondingAuthor":false,"prefix":"","firstName":"Xudong","middleName":"","lastName":"Qu","suffix":""},{"id":625549468,"identity":"525cb423-354b-4308-93b8-650c545ecd06","order_by":3,"name":"Lin Cheng","email":"","orcid":"","institution":"Xi’an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Cheng","suffix":""},{"id":625549470,"identity":"dd37e930-025c-4fe8-bf9f-3fa89d60b2d2","order_by":4,"name":"Chunhui Ma","email":"","orcid":"","institution":"Xi’an University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Chunhui","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2026-04-08 15:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9359121/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9359121/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107485456,"identity":"6527ee36-e04f-4b3f-a872-51a81e29ab6c","added_by":"auto","created_at":"2026-04-22 02:34:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1826012,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9359121/v1_covered_0bac3591-c7cc-4f9b-8552-d0007e25f15b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Efficient system reliability analysis of landslide-pipeline interaction via active learning Kriging with sequential importance sampling","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"stochastic-environmental-research-and-risk-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"serr","sideBox":"Learn more about [Stochastic Environmental Research and Risk Assessment](https://www.springer.com/journal/477)","snPcode":"477","submissionUrl":"https://submission.nature.com/new-submission/477/3","title":"Stochastic Environmental Research and Risk Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"System reliability analysis, Active learning Kriging, Sequential importance sampling, Coupled Eulerian-Lagrangian (CEL), Landslide-pipeline interaction, Spatial variability","lastPublishedDoi":"10.21203/rs.3.rs-9359121/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9359121/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSystem reliability assessment of buried pipelines subjected to landslide hazards is computationally challenging due to high-dimensional spatial variability and complex nonlinear large-deformation responses. This paper proposes AK-SYS-SIS, integrating Sequential Importance Sampling (SIS) into the Active Learning Kriging framework for system reliability analysis. The method constructs an approximate optimal sampling density to adaptively guide candidates toward critical boundaries of multiple failure modes, while a composite learning strategy selectively updates only the most critical surrogate component. Two stochastic large-deformation finite element frameworks, RCEL-OLHS and GCRCEL-OLHS, are developed within the Coupled Eulerian-Lagrangian (CEL) technique to simulate earthquake-induced and rainfall-induced landslide\u0026ndash;pipeline interactions, respectively. These frameworks incorporate Karhunen\u0026ndash;Lo\u0026egrave;ve random field discretization, strain-softening constitutive models via Abaqus VUSDFLD/UMAT subroutines, modified Mohr-Coulomb plasticity, and soil rotating anisotropy, with GPU-accelerated random field generation for the rainfall scenario. 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