Structural optimization for the frame structures based on the DNN models and Bayesian optimization method in generative design

Structural optimization for the frame structures based on the DNN models and Bayesian optimization method in generative design | 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 Structural optimization for the frame structures based on the DNN models and Bayesian optimization method in generative design Zixuan Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5697471/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 This study presents a framework to evaluate the structural optimization of the frame structures based on the Deep Neural Networks (DNNs), numerical simulations, and Bayesian optimization method. The developed deep learning-based (DNN) models are developed with the input data obtained from the numerical simulations, and the performance outputs of mass (M), displacement (D), and safety factor (FS), which are able to achieve accurate predictions of nonlinear structure-parameters relationships using deep neural networks. The optimized designs with optimization objectives as deflection is obtained efficiently and precisely using Bayesian Optimization algorithm. Moreover, the present study demonstrates that the DNN-based model integrated with generative design outcomes and Bayesian optimization algorithm can provide a promising tool for the design and optimization of frame structures in product design. Physical sciences/Engineering/Civil engineering Physical sciences/Mathematics and computing/Scientific data structural optimization Deep Neural Networks numerical simulations Bayesian optimization 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-5697471","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":399147913,"identity":"a820de88-e069-45bc-a751-952678ba1152","order_by":0,"name":"Zixuan Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACNvaG5AcfKmzk5BkOHyBOCx/PgWeGM86kGRs2HksgTouchOMDad62w4kNh88YEOkwCeYEoC2HGRvbzny88YbBTk63gZAW6bYEoF/Smdl5zm62nMOQbGx2gJAWmTMgW6zZGGec3SbNw3AgcRtBLRL5H4B+YeZhuP/mGbFaEhKAWpwlGA6cYSNSC8+BNFAgGxg2HDO2nGNAhF/k2yFRWT+f4fDDG28q7OQIakEBEjxERg2yFlJ1jIJRMApGwYgAAISCSMwNApFJAAAAAElFTkSuQmCC","orcid":"","institution":"Institution of Engineering and Technology, University of Strathclyde","correspondingAuthor":true,"prefix":"","firstName":"Zixuan","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-12-23 07:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5697471/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5697471/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75690791,"identity":"61264bc3-7632-4496-aee6-1ca0b1015300","added_by":"auto","created_at":"2025-02-07 07:09:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1172845,"visible":true,"origin":"","legend":"","description":"","filename":"SRJournalpaper202517.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5697471/v1_covered_e4d4ba41-1058-486a-9f1c-dd8a836778bf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Structural optimization for the frame structures based on the DNN models and Bayesian optimization method in generative design","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":"structural optimization, Deep Neural Networks, numerical simulations, Bayesian optimization","lastPublishedDoi":"10.21203/rs.3.rs-5697471/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5697471/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a framework to evaluate the structural optimization of the frame structures based on the Deep Neural Networks (DNNs), numerical simulations, and Bayesian optimization method. 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