Research on mechanical properties prediction of regenerative ultra-high performance Concrete based on machine learning 1

Research on mechanical properties prediction of regenerative ultra-high performance Concrete based on machine learning 1 | 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 Research on mechanical properties prediction of regenerative ultra-high performance Concrete based on machine learning 1 yihong xu, feng fan, qiang li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9201001/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 recycling technology of waste concrete, the preparation of regenerated aggregate and regenerated fine powder, and the use of them to replace quartz sand and cement, and the preparation of regenerated ultra-high performance concrete (R-UHPC) is a research hotspot in recent years. In this paper, a new machine learning method is proposed to predict the compressive strength performance of R-UHPC by using XGBoost algorithm. The compression performance of R-UHPC is predicted by using cement substitute material and aggregate substitute material index. More than 200 groups of literature data at home and abroad are trained, and the good fit is above 0.93, and the error between the predicted value of the model and the literature value is within 5%. The error between the model prediction value and the literature value is within 5%, indicating that the established XGBoost model has good prediction effect. By establishing the Python program of improved MMA matching, 21 groups of specimens were made, and the mechanical test study was carried out. The error between the predicted value of the model and the actual value of the test was within 5%. The results show that the established XGBoost model has good prediction effect Physical sciences/Engineering Physical sciences/Materials science recycled materials Recycled ultra-high performance concrete (R-UHPC) Python Machine learning XGBoost algorithm 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-9201001","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":623148907,"identity":"8086dd09-7ee5-4fe2-8870-368d710aac67","order_by":0,"name":"yihong xu","email":"","orcid":"","institution":"KEYI COLLEGE OF ZHEJIANG SCI-TECH UNIVERSITY","correspondingAuthor":false,"prefix":"","firstName":"yihong","middleName":"","lastName":"xu","suffix":""},{"id":623148908,"identity":"2736bea3-8039-4d03-b1df-248b0294f3cd","order_by":1,"name":"feng fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYDACdsYGEMXPwMB8gEgtzEAtQLWSDQxsCcRqAWKIFh4D4nTwNzO3Pf5QcUeCf3bPxxtvGOzkdBsIaJE4zNhucODMMwmJO2c3W85hSDY2O0BAiwEzY5vEwbbDdQw3crdJ8zAcSNxGrBYJ+Rs5z0jUYnAjh404LUC/tEmcOXNYwvBGmrHlHAMi/MLf3v5MoqLisITcjeSHN95U2MkR1IJqJbFRg6yFVB2jYBSMglEwIgAATrBA9jEF0lUAAAAASUVORK5CYII=","orcid":"","institution":"KEYI COLLEGE OF ZHEJIANG SCI-TECH UNIVERSITY","correspondingAuthor":true,"prefix":"","firstName":"feng","middleName":"","lastName":"fan","suffix":""},{"id":623148909,"identity":"76482168-caaa-4605-a897-5cb8ea3d30e3","order_by":2,"name":"qiang li","email":"","orcid":"","institution":"KEYI COLLEGE OF ZHEJIANG SCI-TECH UNIVERSITY","correspondingAuthor":false,"prefix":"","firstName":"qiang","middleName":"","lastName":"li","suffix":""}],"badges":[],"createdAt":"2026-03-23 13:11:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9201001/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9201001/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107602776,"identity":"56e3f435-c30e-4c3d-b097-87e00b0f08f6","added_by":"auto","created_at":"2026-04-23 06:56:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":462155,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchonpredictionofRUHPCbasedonML.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9201001/v1_covered_201ec65f-e16a-48f2-94f9-ceae4c6e05a6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on mechanical properties prediction of regenerative ultra-high performance Concrete based on machine learning 1","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":"recycled materials, Recycled ultra-high performance concrete (R-UHPC), Python, Machine learning, XGBoost algorithm","lastPublishedDoi":"10.21203/rs.3.rs-9201001/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9201001/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBased on the recycling technology of waste concrete, the preparation of regenerated aggregate and regenerated fine powder, and the use of them to replace quartz sand and cement, and the preparation of regenerated ultra-high performance concrete (R-UHPC) is a research hotspot in recent years. 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