Evaluating mechanical and environmental impacts of sustainable natural fiber reinforced recycled aggregate concrete incorporating supervised machine learning methods

Evaluating mechanical and environmental impacts of sustainable natural fiber reinforced recycled aggregate concrete incorporating supervised machine learning methods | 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 Evaluating mechanical and environmental impacts of sustainable natural fiber reinforced recycled aggregate concrete incorporating supervised machine learning methods Rahat Aayaz, Md. Habibur Rahman Sobuz, Md. Kawsarul Islam Kabbo, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6586869/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 Construction industry is increasingly focusing on sustainable decarbonized concrete using alternative materials. One promising innovation is natural fiber recycled aggregate concrete (NFRAC), which combines recycled aggregates with natural fibers to enhance concrete performance and reduce the carbon footprint from quarrying and fiber production. This study analyzes 534 data points from existing literature to predict the compressive strength of NFRAC made with fibers from jute, sisal, kenaf, ramie, coir, and bamboo, across varying water-cement ratios and curing ages. Five machine learning models: eXtreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), Multilayer Perceptron (MLP), and Categorical Boosting (CAT), were employed to predict compressive strength, with hyperparameters optimized using Particle Swarm Optimization (PSO). SHapley Additive exPlanations (SHAP) and partial dependency plots (PDP) assessed the impact of key factors, showing that the water-binder ratio significantly affects compressive strength. The XGB model achieved the best results with an RMSE of 4.2 MPa and R² of 0.94. Life cycle analysis indicated that using 25% recycled concrete aggregate (RCA) and natural fibers reduces embodied CO₂ emissions by 2.7%; with 50% RCA replacement, the reduction reaches 5.4%. A cost-benefit analysis revealed that NFRAC offers significant economic advantages over traditional concrete, particularly at higher replacement rates. The study validated the machine learning models and established a user-friendly web interface for predicting NFRAC compressive strength. This integration of advanced machine learning, carbon analysis, and economic evaluation highlights the potential for adopting natural fibers and recycled aggregates in sustainable concrete construction, effectively mitigating environmental impact. Natural Fiber Recycled Aggregate Concrete Machine Learning Particle Swarm Optimization SHapley Additive exPlanations Partial Dependency Plots Life cycle analysis 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. 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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-6586869","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453483172,"identity":"d41f43e9-025d-416b-b10b-930983618931","order_by":0,"name":"Rahat Aayaz","email":"","orcid":"","institution":"Khulna University of Engineering \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Rahat","middleName":"","lastName":"Aayaz","suffix":""},{"id":453483173,"identity":"9b780f48-439d-417f-b4a4-ef24081a3554","order_by":1,"name":"Md. 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One promising innovation is natural fiber recycled aggregate concrete (NFRAC), which combines recycled aggregates with natural fibers to enhance concrete performance and reduce the carbon footprint from quarrying and fiber production. This study analyzes 534 data points from existing literature to predict the compressive strength of NFRAC made with fibers from jute, sisal, kenaf, ramie, coir, and bamboo, across varying water-cement ratios and curing ages. Five machine learning models: eXtreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), Multilayer Perceptron (MLP), and Categorical Boosting (CAT), were employed to predict compressive strength, with hyperparameters optimized using Particle Swarm Optimization (PSO). SHapley Additive exPlanations (SHAP) and partial dependency plots (PDP) assessed the impact of key factors, showing that the water-binder ratio significantly affects compressive strength. The XGB model achieved the best results with an RMSE of 4.2 MPa and R\u0026sup2; of 0.94. Life cycle analysis indicated that using 25% recycled concrete aggregate (RCA) and natural fibers reduces embodied CO₂ emissions by 2.7%; with 50% RCA replacement, the reduction reaches 5.4%. A cost-benefit analysis revealed that NFRAC offers significant economic advantages over traditional concrete, particularly at higher replacement rates. The study validated the machine learning models and established a user-friendly web interface for predicting NFRAC compressive strength. This integration of advanced machine learning, carbon analysis, and economic evaluation highlights the potential for adopting natural fibers and recycled aggregates in sustainable concrete construction, effectively mitigating environmental impact.\u003c/p\u003e","manuscriptTitle":"Evaluating mechanical and environmental impacts of sustainable natural fiber reinforced recycled aggregate concrete incorporating supervised machine learning methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 04:04:57","doi":"10.21203/rs.3.rs-6586869/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":"cf53ab2a-4d91-49fa-8f2a-fd902f0fac68","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-15T13:38:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 04:04:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6586869","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6586869","identity":"rs-6586869","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}