The mechanical property changes of bridge structures using a Viscoelastic Model and Machine Learning Techniques

The mechanical property changes of bridge structures using a Viscoelastic Model and Machine Learning Techniques | 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 The mechanical property changes of bridge structures using a Viscoelastic Model and Machine Learning Techniques Thanh Q. Nguyen, Thuy T. Nguyen, Phuoc T. Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4931369/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 The article evaluates changes in the mechanical structure of a bridge using a model of a viscoelastic oscillator system. The study uses the vibration spectrum of the bridge structure through vibration signals as a research model. The vibration response spectrum of the bridge is considered in two independent states in practice, including the bending and torsional states to investigate the change in material mechanical properties. The first proposal of this study is to use the actual vibration model of the bridge to build a response spectrum that is as close to reality as possible with the support of machine learning. Using a CNN network model, the draught was optimised and enriched with information extracted from the vibration spectrum. The features of the spectrum serve as a basis for determining the existence of defects in the structure. From there, this research model allows the author to explore the influence of mechanical components on the structure through the proposed dissipation coefficient. The second proposal is to build a model of the value of the regression surface of the dissipation coefficient in the structural material. This quantity helps to evaluate the degree of data dispersion of the dissipation coefficient obtained from the response spectrum data. The study shows that the proposed quantity can evaluate the model for good practical results. In the future, this research model can be applied to many different types of structures with complex load states. bridge vibration modal analysis material properties defect detection dispersion coefficient regression analysis CNN network model 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-4931369","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":354416310,"identity":"d2f0bc4b-bab2-4be8-95ae-b22a44410b85","order_by":0,"name":"Thanh Q. Nguyen","email":"","orcid":"","institution":"Ho Chi Minh City Open University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Thanh","middleName":"Q.","lastName":"Nguyen","suffix":""},{"id":354416311,"identity":"00959915-ed43-416d-a06c-f0ce7cc92be6","order_by":1,"name":"Thuy T. 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The second proposal is to build a model of the value of the regression surface of the dissipation coefficient in the structural material. This quantity helps to evaluate the degree of data dispersion of the dissipation coefficient obtained from the response spectrum data. The study shows that the proposed quantity can evaluate the model for good practical results. 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