Nonlinear Finite Element and Machine Learning-Based Prediction of Circular CFRP-Confined Reinforced and Plain Concrete Columns under Axial Compression

Nonlinear Finite Element and Machine Learning-Based Prediction of Circular CFRP-Confined Reinforced and Plain Concrete Columns under Axial Compression | 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 Nonlinear Finite Element and Machine Learning-Based Prediction of Circular CFRP-Confined Reinforced and Plain Concrete Columns under Axial Compression Besukal Befikadu Zewudie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8360189/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract This study presents a comprehensive nonlinear finite element study on the behavior of circular CFRP-confined reinforced and plain concrete columns under concentric static loading. A total of 65 test models, which consist of a group of FRP-confined plain concrete, concrete with longitudinal and circular hoop reinforcements, and spiral hoop-reinforced concrete, were designed for this study. The accuracy of the proposed finite element model was verified by comparing it with the existing experimental test results. The impact of unconfined concrete strength, hoop reinforcement ratio, thickness of FRP, and spiral hoop spacing on the confinement effectiveness, load-carrying capacity, and ductility behavior of circular FRP-confined concrete columns was demonstrated. The parametric study revealed that increasing unconfined concrete strength enhances the axial load capacity of FRP-confined concrete columns, but low-strength confined concrete achieves a higher strength enhancement ratio than high-grade concrete. From the study, it’s also found that the confinement effectiveness of hoop reinforcement is mainly dependent on the confining FRP thickness. Based on existing experimental test results and FEA results presented in this paper, a new predictive analytical model for determining circular FRP-confined concrete columns' peak stress with the associated strain was also developed. Furthermore, machine learning (ML) models for predicting the peak axial load and ultimate strain at the tensile rupture of FRP were developed using four different machine learning techniques. The predictive performance of the proposed machine learning models was evaluated by six performance metrics indices of statistical parameters. Confined concrete Carbon reinforced polymer tensile rupture Machine learning model Ultimate strain Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 26 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviews received at journal 11 Feb, 2026 Reviews received at journal 06 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers invited by journal 02 Feb, 2026 Editor assigned by journal 02 Jan, 2026 Submission checks completed at journal 02 Jan, 2026 First submitted to journal 14 Dec, 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. 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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-8360189","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584659854,"identity":"42d5ddcd-0d57-489e-aaf0-50de3112b3f0","order_by":0,"name":"Besukal Befikadu Zewudie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACAxDB2MDAL8HeAOJaEK9FcgbPARBXghQtEgkgJhFazNnbn0kw7rCRkJz5/OqGHwUSDPzt3Ql4tVj2nDGTYDyTJiEtnVN2swfoMIkzZzfgd9iNHDYJxrbDdXLSOWk3eIBaDCRyCWi5/xzosLb/EnKSZ9Ju/iFKyw0GoMPaDkhIS7Afu02ULZY9OcYWiWeSJSR7cthuyxhI8BD0izn78Yc3Pu6wk5A4fvzZzTd/bOT423vxawGDBDDJA44jHsLKEYD9ASmqR8EoGAWjYAQBAJLJQ/hdVQb5AAAAAElFTkSuQmCC","orcid":"","institution":"Jimma Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Besukal","middleName":"Befikadu","lastName":"Zewudie","suffix":""}],"badges":[],"createdAt":"2025-12-14 20:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8360189/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8360189/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101878578,"identity":"0e09652a-a1cb-46c9-b739-43b3ac66bac4","added_by":"auto","created_at":"2026-02-04 14:40:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2234995,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptdraftforreview.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8360189/v1_covered_76d7e303-d4e3-41ad-8ba4-f16df618fe60.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eNonlinear Finite Element and Machine Learning-Based Prediction of Circular CFRP-Confined Reinforced and Plain Concrete Columns under Axial Compression\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-civil-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Civil Engineering](https://www.springer.com/journal/44290)","snPcode":"44290","submissionUrl":"https://submission.nature.com/new-submission/44290","title":"Discover Civil Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Confined concrete, Carbon reinforced polymer, tensile rupture, Machine learning model, Ultimate strain","lastPublishedDoi":"10.21203/rs.3.rs-8360189/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8360189/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a comprehensive nonlinear finite element study on the behavior of circular CFRP-confined reinforced and plain concrete columns under concentric static loading. A total of 65 test models, which consist of a group of FRP-confined plain concrete, concrete with longitudinal and circular hoop reinforcements, and spiral hoop-reinforced concrete, were designed for this study. The accuracy of the proposed finite element model was verified by comparing it with the existing experimental test results. The impact of unconfined concrete strength, hoop reinforcement ratio, thickness of FRP, and spiral hoop spacing on the confinement effectiveness, load-carrying capacity, and ductility behavior of circular FRP-confined concrete columns was demonstrated. The parametric study revealed that increasing unconfined concrete strength enhances the axial load capacity of FRP-confined concrete columns, but low-strength confined concrete achieves a higher strength enhancement ratio than high-grade concrete. From the study, it\u0026rsquo;s also found that the confinement effectiveness of hoop reinforcement is mainly dependent on the confining FRP thickness. Based on existing experimental test results and FEA results presented in this paper, a new predictive analytical model for determining circular FRP-confined concrete columns' peak stress with the associated strain was also developed. Furthermore, machine learning (ML) models for predicting the peak axial load and ultimate strain at the tensile rupture of FRP were developed using four different machine learning techniques. The predictive performance of the proposed machine learning models was evaluated by six performance metrics indices of statistical parameters.\u003c/p\u003e","manuscriptTitle":"Nonlinear Finite Element and Machine Learning-Based Prediction of Circular CFRP-Confined Reinforced and Plain Concrete Columns under Axial Compression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-04 14:39:41","doi":"10.21203/rs.3.rs-8360189/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-23T12:21:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-26T11:00:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T16:42:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174285353827033064015240709292878228844","date":"2026-02-16T00:04:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136632135566226405371565647807190361885","date":"2026-02-14T15:37:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17896217712203354355606320405821710606","date":"2026-02-14T09:10:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-11T11:47:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T07:36:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232855552272362105738748343186084752054","date":"2026-02-05T23:22:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"83123491285613538818933845040695775445","date":"2026-02-02T15:28:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-02T13:01:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-02T13:06:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-02T13:03:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Civil Engineering","date":"2025-12-14T20:34:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-civil-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Civil Engineering](https://www.springer.com/journal/44290)","snPcode":"44290","submissionUrl":"https://submission.nature.com/new-submission/44290","title":"Discover Civil Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"81167fa2-6166-451d-83ab-3c9072c3538c","owner":[],"postedDate":"February 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T12:25:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-04 14:39:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8360189","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8360189","identity":"rs-8360189","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}