Modeling of Predcition Model for Hybrid Fibre Reinforced Concrete (HFRC)

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Abstract

Abstract Since, there was numerous models are available for prediction of mechanical properties of hybrid fibres. However, they are not used the physical properties of the fibres as input for the model. Most of the models were limited to 10 input variables. Hence, this study uses more than 10 inputs to developed the prediction model for the mechanical properties of the HFRC (Steel Fibre, Polypropylene and Polyethylene Terephthalate Fibre). The model uses the data from early literatures which consist of cement, fine aggregate, coarse aggregate, water, days of curing, superplasticizer, steel fibre content, steel fibre length, polypropylene content, polypropylene length, Polyethylene Terephthalate Fibre ( PET) content, PET fibre length, compressive strength, split tensile strength and flexural strength. A total of 162 specimens comprising cube, cylinder and prisms were cast and subsequently tested as part of experimental investigations. In the research, four different machine learning algorithms Gradient Boosting, Random Forest, Linear Regression and XGBoost were employed to create predictive models for three distinct parameters: compressive strength (Model-1), split tensile strength (Model-2) and flexural strength (Model-3). These models underwent rigorous evaluation using various performance metrics including R 2 score, Train score, Test score and Root Mean Square Error (RMSE). Notably, Gradient Boosting emerged as the top performer in predicting compressive strength achieving an impressive R 2 score of 0.95. For predicting tensile strength, both the Gradient Boosting and XGBoost algorithms demonstrate superior performance, achieving R 2 scores of 0.92 and 0.90 respectively. These results emphasize their ability to capture involved relationships within HFRC compositions. Regarding flexural strength prediction, the XGBoost algorithm outperforms others with an R 2 score of 0.89, showcasing its proficiency in modelling the complex interactions among material constituents and structural behaviour. In conclusion, the proposed model effectively predicts compressive strength, split tensile strength and flexural strength with minimal error.
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Modeling of Predcition Model for Hybrid Fibre Reinforced Concrete (HFRC) | 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 Modeling of Predcition Model for Hybrid Fibre Reinforced Concrete (HFRC) M. Vadivel, S. Sundar, M. Rama, C. Vinothini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7589920/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 Since, there was numerous models are available for prediction of mechanical properties of hybrid fibres. However, they are not used the physical properties of the fibres as input for the model. Most of the models were limited to 10 input variables. Hence, this study uses more than 10 inputs to developed the prediction model for the mechanical properties of the HFRC (Steel Fibre, Polypropylene and Polyethylene Terephthalate Fibre). The model uses the data from early literatures which consist of cement, fine aggregate, coarse aggregate, water, days of curing, superplasticizer, steel fibre content, steel fibre length, polypropylene content, polypropylene length, Polyethylene Terephthalate Fibre ( PET) content, PET fibre length, compressive strength, split tensile strength and flexural strength. A total of 162 specimens comprising cube, cylinder and prisms were cast and subsequently tested as part of experimental investigations. In the research, four different machine learning algorithms Gradient Boosting, Random Forest, Linear Regression and XGBoost were employed to create predictive models for three distinct parameters: compressive strength (Model-1), split tensile strength (Model-2) and flexural strength (Model-3). These models underwent rigorous evaluation using various performance metrics including R 2 score, Train score, Test score and Root Mean Square Error (RMSE). Notably, Gradient Boosting emerged as the top performer in predicting compressive strength achieving an impressive R 2 score of 0.95. For predicting tensile strength, both the Gradient Boosting and XGBoost algorithms demonstrate superior performance, achieving R 2 scores of 0.92 and 0.90 respectively. These results emphasize their ability to capture involved relationships within HFRC compositions. Regarding flexural strength prediction, the XGBoost algorithm outperforms others with an R 2 score of 0.89, showcasing its proficiency in modelling the complex interactions among material constituents and structural behaviour. In conclusion, the proposed model effectively predicts compressive strength, split tensile strength and flexural strength with minimal error. Physical sciences/Engineering Physical sciences/Materials science Hybrid Fibre-Reinforced Concrete (HFRC) Machine Learning (ML) Compressive strength Split tensile strength Flexural strength Civil Engineering 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-7589920","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":524173684,"identity":"07649a94-1af5-4905-9098-6908ab92d00e","order_by":0,"name":"M. 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(HFRC)\u003c/p\u003e","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":"Hybrid Fibre-Reinforced Concrete (HFRC); Machine Learning (ML); Compressive strength, Split tensile strength, Flexural strength, Civil Engineering","lastPublishedDoi":"10.21203/rs.3.rs-7589920/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7589920/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSince, there was numerous models are available for prediction of mechanical properties of hybrid fibres. However, they are not used the physical properties of the fibres as input for the model. Most of the models were limited to 10 input variables. Hence, this study uses more than 10 inputs to developed the prediction model for the mechanical properties of the HFRC (Steel Fibre, Polypropylene and Polyethylene Terephthalate Fibre). The model uses the data from early literatures which consist of cement, fine aggregate, coarse aggregate, water, days of curing, superplasticizer, steel fibre content, steel fibre length, polypropylene content, polypropylene length, Polyethylene Terephthalate Fibre \u003cb\u003e(\u003c/b\u003ePET) content, PET fibre length, compressive strength, split tensile strength and flexural strength. A total of 162 specimens comprising cube, cylinder and prisms were cast and subsequently tested as part of experimental investigations. In the research, four different machine learning algorithms Gradient Boosting, Random Forest, Linear Regression and XGBoost were employed to create predictive models for three distinct parameters: compressive strength (Model-1), split tensile strength (Model-2) and flexural strength (Model-3). These models underwent rigorous evaluation using various performance metrics including R\u003csup\u003e2\u003c/sup\u003e score, Train score, Test score and Root Mean Square Error (RMSE). Notably, Gradient Boosting emerged as the top performer in predicting compressive strength achieving an impressive R\u003csup\u003e2\u003c/sup\u003e score of 0.95. For predicting tensile strength, both the Gradient Boosting and XGBoost algorithms demonstrate superior performance, achieving R\u003csup\u003e2\u003c/sup\u003e scores of 0.92 and 0.90 respectively. These results emphasize their ability to capture involved relationships within HFRC compositions. Regarding flexural strength prediction, the XGBoost algorithm outperforms others with an R\u003csup\u003e2\u003c/sup\u003e score of 0.89, showcasing its proficiency in modelling the complex interactions among material constituents and structural behaviour. In conclusion, the proposed model effectively predicts compressive strength, split tensile strength and flexural strength with minimal error.\u003c/p\u003e","manuscriptTitle":"Modeling of Predcition Model for Hybrid Fibre Reinforced Concrete (HFRC)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 18:06:46","doi":"10.21203/rs.3.rs-7589920/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"b48a3da0-a436-4097-b2b5-96306617e3ab","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55855916,"name":"Physical sciences/Engineering"},{"id":55855917,"name":"Physical sciences/Materials science"}],"tags":[],"updatedAt":"2026-03-12T06:56:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 18:06:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7589920","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7589920","identity":"rs-7589920","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

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