Research on intelligent semi-active control algorithms and seismic reliability based on machine learning

Research on intelligent semi-active control algorithms and seismic reliability based on machine learning | 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 Research on intelligent semi-active control algorithms and seismic reliability based on machine learning Zhongyuan Xiao, Jianguo Xu, Li Wang, Liang Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4291641/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 Aiming to address the shortcomings of existing semi-active control algorithms with poor robustness and the limited generalization ability of current evaluation methods based on deterministic analysis, a novel approach based on probability density evolution is proposed. This method is designed to assess the seismic reliability, enabling a more comprehensive evaluation of the control effectiveness of aqueduct structures. Building upon this, an intelligent semi-active control algorithm leveraging machine learning is introduced. The algorithm is further validated through engineering case studies to investigate semi-active control strategies in response to random seismic events. The results show that the seismic reliability of the machine learning-based semi-active control algorithm is significantly higher than that of the uncontrolled state for the same failure threshold under random seismic effects. Semi-active control Probability density evolution BP neural network Modified Optimization Algorithm for Sand Cat Colonies Seismic Reliability 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-4291641","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":293505177,"identity":"acfa5a98-1dfb-4345-83ab-6f8a1e4deb25","order_by":0,"name":"Zhongyuan Xiao","email":"data:image/png;base64,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","orcid":"","institution":"Zhengzhou University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zhongyuan","middleName":"","lastName":"Xiao","suffix":""},{"id":293505181,"identity":"2be34070-aa1f-4157-908b-e934e7ec805d","order_by":1,"name":"Jianguo Xu","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianguo","middleName":"","lastName":"Xu","suffix":""},{"id":293505189,"identity":"fa204089-bd94-4bb3-8ede-92f0599bcee0","order_by":2,"name":"Li Wang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Wang","suffix":""},{"id":293505192,"identity":"f8f8dcda-4836-4e95-8497-476b4db1db22","order_by":3,"name":"Liang Huang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-04-19 07:48:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4291641/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4291641/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55294577,"identity":"5f692faa-1b67-483c-aa47-8060cb1f7d84","added_by":"auto","created_at":"2024-04-25 10:09:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7710313,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4291641/v1_covered_5383a002-d831-49b8-9f81-99e59aaf52fe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on intelligent semi-active control algorithms and seismic reliability based on machine learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Semi-active control, Probability density evolution, BP neural network, Modified Optimization Algorithm for Sand Cat Colonies, Seismic Reliability","lastPublishedDoi":"10.21203/rs.3.rs-4291641/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4291641/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAiming to address the shortcomings of existing semi-active control algorithms with poor robustness and the limited generalization ability of current evaluation methods based on deterministic analysis, a novel approach based on probability density evolution is proposed. 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