Analysis of forced vibration data using output-error methods

Analysis of forced vibration data using output-error methods | 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Article Analysis of forced vibration data using output-error methods Mehran Pourgholi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2646113/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 Based on the linearization of the structure's vibration equation in the state space, the stochastic subspace (SSI) approach is often used for system identification in the time domain of structures. As a consequence of using singular value decomposition (SVD) and QR factorization, the non-linear optimization solution may be avoided, and the identification issue can be solved as a linear least-squares problem. Although SSI does not explicitly minimize a cost function to produce the system matrices, the statistical analysis is significantly more involved for subspace approaches. Alternatively, in system identification, one might choose an output-error method (OEM), whereby the model parameters are repeatedly tweaked to match the outputs of the simulated model and the observed system. The purpose of this study is to modify the OEM to obtain structural features in the following manner: First, to reduce the number of optimization iterations, the initial term is derived using the SSI. Second, the objective function's nonlinearity is reduced by considering the second-order derivatives as a linear system to optimize parameters using the Gauss-Newton approach. Finally, perform a gradient project minimization in state-space systems to prevent non-injectivity. After applying OEM to the results of a model of a three-story structure activated by seismic acceleration at SNR = 1dB, the model's damping ratio and mode shapes became more precise. System Identification Output-Error method Gauss-Newton gradient project State Space 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. 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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-2646113","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":180273470,"identity":"5e1102aa-5af3-48a3-a504-4f2726ad6f86","order_by":0,"name":"Mehran Pourgholi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYLACxgYGGTb2xga4wAFitPCw8RwkVQuDRAKRbjJn7zH7+HWHDQ+f5OPGzzx/7Bj42w8wHq7Ao8Wy54zxbNkzaTxs0onN0rxtyQwSZxIYDp7Bo8XgRo4xs2TbYZCWBmneBmYGhhsMDEjewqdF8mDzb54/9QzyxGhh/AjSIsHYJs3DdhgoQkCLZc+xYmbGNqBfeBLbLOe2HecxPJPYgFeLOXvzZsafbTZy8u3HH99486daTu744cMf8ToMiJl5kAR4wNGED4C0MP7Aq2QUjIJRMApGPAAAgxBI1gosYYMAAAAASUVORK5CYII=","orcid":"","institution":"Islamic Azad University, Sarab","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mehran","middleName":"","lastName":"Pourgholi","suffix":""}],"badges":[],"createdAt":"2023-03-02 07:29:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2646113/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2646113/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":33881118,"identity":"15ad0aa8-60bb-467f-8d14-f10a5191919c","added_by":"auto","created_at":"2023-03-07 06:31:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":525940,"visible":true,"origin":"","legend":"","description":"","filename":"APP.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2646113/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of forced vibration data using output-error methods","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":"System Identification, Output-Error method, Gauss-Newton, gradient project, State Space","lastPublishedDoi":"10.21203/rs.3.rs-2646113/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2646113/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBased on the linearization of the structure's vibration equation in the state space, the stochastic subspace (SSI) approach is often used for system identification in the time domain of structures. 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