Fusion-Based Data-Driven Strength Prediction of Bamboo Fiber-Reinforced Concrete for Sustainable Construction

Fusion-Based Data-Driven Strength Prediction of Bamboo Fiber-Reinforced Concrete for Sustainable Construction | 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 Fusion-Based Data-Driven Strength Prediction of Bamboo Fiber-Reinforced Concrete for Sustainable Construction G. R. Priyasri, Suneel Gollapalli, P. Jagannadha Varma, Senthil Kumar Muniasamy, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9126769/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 study develops a machine learning-based predictive framework to estimate the compressive strength of bamboo fiber reinforced concrete (BFRC). A literature-derived dataset was compiled from key parameters influencing BFRC performance, including bamboo fiber dosage (0.25–1.00%), fine aggregate content (646–649 kg/m³), slump values (44.6–98.6 mm), and curing ages of 7, 14, and 28 days. The dataset indicates average compressive strengths of 30.18 MPa, 38.51 MPa, and 47.30 MPa at the respective curing periods, reflecting progressive hydration and densification of the cementitious matrix. Machine learning models were developed to capture nonlinear relationships between mixture parameters, fiber dosage, and strength development. The results demonstrate strong predictive capability, with model performance showing high accuracy in estimating compressive strength across different curing stages. The analysis reveals that bamboo fiber content significantly influences the mechanical behaviour of BFRC. Strength increases with fiber addition up to a moderate dosage due to effective crack bridging and stress redistribution within the matrix, while excessive fiber content reduces workability and compaction efficiency. The findings illustrate an optimal bamboo fiber dosage of approximately 0.75%, which provides improved compressive strength while maintaining acceptable workability. The predictive framework offers a reliable decision -support tool for optimizing BFRC mix design and promoting sustainable concrete technologies. Bamboo fiber concrete Machine learning Compressive strength Sustainable materials Predictive modelling 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-9126769","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612014978,"identity":"44f2122f-8723-43e1-b170-e8b539ff8cad","order_by":0,"name":"G. R. 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A literature-derived dataset was compiled from key parameters influencing BFRC performance, including bamboo fiber dosage (0.25\u0026ndash;1.00%), fine aggregate content (646\u0026ndash;649 kg/m\u0026sup3;), slump values (44.6\u0026ndash;98.6 mm), and curing ages of 7, 14, and 28 days. The dataset indicates average compressive strengths of 30.18 MPa, 38.51 MPa, and 47.30 MPa at the respective curing periods, reflecting progressive hydration and densification of the cementitious matrix. Machine learning models were developed to capture nonlinear relationships between mixture parameters, fiber dosage, and strength development. The results demonstrate strong predictive capability, with model performance showing high accuracy in estimating compressive strength across different curing stages. The analysis reveals that bamboo fiber content significantly influences the mechanical behaviour of BFRC. Strength increases with fiber addition up to a moderate dosage due to effective crack bridging and stress redistribution within the matrix, while excessive fiber content reduces workability and compaction efficiency. The findings illustrate an optimal bamboo fiber dosage of approximately 0.75%, which provides improved compressive strength while maintaining acceptable workability. 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