Coupled dynamic interaction analysis of Box-girder bridge and High speed train using Artificial neural network framework

Coupled dynamic interaction analysis of Box-girder bridge and High speed train using Artificial neural network framework | 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 Coupled dynamic interaction analysis of Box-girder bridge and High speed train using Artificial neural network framework Virajan Verma, Khair Ul Faisal Wani, Nallasivam K., Karan Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8161865/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract This study focuses on the complex dynamic interaction between high-speed trains and curved, thin-walled box-girder bridges, where traditional computational approaches are typically insufficient for lengthy parametric simulations. A thorough numerical model was developed by combining a 38-degree-of-freedom train system and a slab track structure with a thin-walled, curved box-beam finite element model. The resulting coupled dynamic simulation data was utilized to train two different Artificial Neural Network (ANN) models that predicted essential structural reactions based on two major input variables, train velocity and subgrade suspension system parameters. The parametric analysis yielded substantial results for dynamic behaviour of the bridge. The model demonstrated that increasing train velocity from 150 km/h to 400 km/h significantly increases all stress resultants such as shear force, bending moment and torsional moment. Changing the subgrade suspension parameters, on the other hand, led in only an insignificant change in the bridge's dynamic response, implying a minor impact on global behaviour. Both ANN frameworks underwent intensive validation, displaying remarkable predictive performance. Diagnostic plots repeatedly revealed near-perfect correlation coefficients (R nearing 1), confirming the robustness of the Levenberg-Marquardt training procedure, which achieved rapid, stable convergence. Furthermore, the Error Histogram analysis confirmed that the network produces unbiased prediction residuals, indicating that the model is reliable and robust for generalization. The successful implementation of the ANN framework offers structural engineers with an accurate, computationally efficient tool for performing quick safety assessments and design optimization on high-speed rail infrastructure. Artificial neural network Bridge-vehicle dynamic interaction Curved box-girder bridge High speed train Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviews received at journal 13 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 03 Dec, 2025 Reviewers agreed at journal 03 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers invited by journal 01 Dec, 2025 Editor assigned by journal 27 Nov, 2025 Submission checks completed at journal 27 Nov, 2025 First submitted to journal 20 Nov, 2025 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-8161865","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553795416,"identity":"bb57aa98-39e5-430f-80d4-431f7951181e","order_by":0,"name":"Virajan 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A thorough numerical model was developed by combining a 38-degree-of-freedom train system and a slab track structure with a thin-walled, curved box-beam finite element model. The resulting coupled dynamic simulation data was utilized to train two different Artificial Neural Network (ANN) models that predicted essential structural reactions based on two major input variables, train velocity and subgrade suspension system parameters. The parametric analysis yielded substantial results for dynamic behaviour of the bridge. The model demonstrated that increasing train velocity from 150 km/h to 400 km/h significantly increases all stress resultants such as shear force, bending moment and torsional moment. Changing the subgrade suspension parameters, on the other hand, led in only an insignificant change in the bridge's dynamic response, implying a minor impact on global behaviour. Both ANN frameworks underwent intensive validation, displaying remarkable predictive performance. Diagnostic plots repeatedly revealed near-perfect correlation coefficients (R nearing 1), confirming the robustness of the Levenberg-Marquardt training procedure, which achieved rapid, stable convergence. Furthermore, the Error Histogram analysis confirmed that the network produces unbiased prediction residuals, indicating that the model is reliable and robust for generalization. The successful implementation of the ANN framework offers structural engineers with an accurate, computationally efficient tool for performing quick safety assessments and design optimization on high-speed rail infrastructure.\u003c/p\u003e","manuscriptTitle":"Coupled dynamic interaction analysis of Box-girder bridge and High speed train using Artificial neural network framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 07:58:25","doi":"10.21203/rs.3.rs-8161865/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-16T16:07:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T15:06:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-14T03:21:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T13:22:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117716888690476674242064086035490484924","date":"2025-12-08T06:56:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109825363993509724016250292891128848551","date":"2025-12-04T02:29:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179825785466940632088369646379789342217","date":"2025-12-04T01:52:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335139123505797013296905345279967971714","date":"2025-12-02T04:10:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-02T02:43:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-27T22:07:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-27T22:06:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Architecture, Structures and Construction","date":"2025-11-20T07:37:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"architecture-structures-and-construction","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Architecture, Structures and Construction](https://www.springer.com/journal/44150)","snPcode":"44150","submissionUrl":"https://submission.nature.com/new-submission/44150/3","title":"Architecture, Structures and Construction","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"22a58b1f-d4b5-4c5d-b963-c5886c6a958a","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T09:39:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 07:58:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8161865","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8161865","identity":"rs-8161865","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}