Performance Evaluation of Sustainable Concrete Incorporating Mineral Fillers: Experimental Insights and Predictive Modeling Using Advanced Machine Learning

Performance Evaluation of Sustainable Concrete Incorporating Mineral Fillers: Experimental Insights and Predictive Modeling Using Advanced Machine Learning | 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 Performance Evaluation of Sustainable Concrete Incorporating Mineral Fillers: Experimental Insights and Predictive Modeling Using Advanced Machine Learning Maaz Khan, Muhammad Faisal Javed, Usama Asif This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6789685/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 Concrete production significantly contributes to environmental concerns due to its high demand for natural resources and carbon emissions. To address these issues, sustainable alternatives such as incorporating quarry dust as a partial replacement for fine aggregates have gained attention. However, optimizing such mixtures while ensuring desirable engineering properties remains challenging. This study investigates the use of quarry dust in concrete through experimental testing and advanced machine learning (ML) modeling to evaluate its performance as an eco-friendly alternative. An experimental program was conducted for two grades of concrete (M15 and M20) to assess the fresh and hardened properties of concrete incorporating quarry dust at replacement levels ranging from 0% to 60%, along with a superplasticizer. Key properties such as workability and compressive, tensile and flexural strength were measured to evaluate the concrete’s performance. The experimental results were then used to develop ML models, specifically employing Gene-Expression Programming (GEP) and Random Forest (RF), to predict concrete mixtures' compressive, flexural and tensile strength based on various mix proportions. The models were validated using statistical performance metrics and sensitivity analysis to ensure their reliability. The predictive performance of RF slightly outperformed GEP, though GEP provided valuable insights through interpretable equations, making it an effective tool for optimizing sustainable concrete mixtures. Sensitivity analysis revealed that the results were well-aligned with existing literature and experimental findings, confirming the models' consistency and credibility. The study demonstrates the potential of soft computing techniques in enhancing the development of sustainable concrete. Quarry dust Gene expression programming Random Forest Compressive strength Flexural strength 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-6789685","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470878094,"identity":"518a6dee-b9b5-4bf8-a6ee-91931d8eeb8f","order_by":0,"name":"Maaz Khan","email":"","orcid":"","institution":"Ghulam Ishaq Khan Institute of Engineering Sciences and Technology","correspondingAuthor":false,"prefix":"","firstName":"Maaz","middleName":"","lastName":"Khan","suffix":""},{"id":470878095,"identity":"2f3a384e-981b-48e0-89ae-4c50e5093a29","order_by":1,"name":"Muhammad Faisal Javed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIie2RsUoDQRCGZzlImolpF+7wXmFECIJiXmWPha0OOQiEiIXKwVqmDfgUNpL2ONBmtT5IY3oLgxYHWrgrIjYbYye4H8zwF/vBPyxAIPAnQYjgwG6Izh+AhA1AdsQPinIKK+lXioVp/vlyvdK/vKueC3GYpBeVPn4pVALd8pqzufIq/P5IxjMhkUymFwnlCHgz5szk/l4GKe61ERJYhdMEgecDzvTEa6QGd19RnGI6XerRh5I+rlfI4CBGUSM0mWZPrhhHp/iL7ZgttT8Tt0jNsoyBFHZQjfYy4z9/2/TqRSFOhulUVqv2TQ773fqqWc2l//zvRO4fOy5lZ5sZwNqvtKkSCAQC/4F3LclMRAeK804AAAAASUVORK5CYII=","orcid":"","institution":"Ghulam Ishaq Khan Institute of Engineering Sciences and Technology","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Faisal","lastName":"Javed","suffix":""},{"id":470878096,"identity":"367aa075-3927-4616-9b56-a56a22dc7f58","order_by":2,"name":"Usama Asif","email":"","orcid":"","institution":"University of Maryland, College Park","correspondingAuthor":false,"prefix":"","firstName":"Usama","middleName":"","lastName":"Asif","suffix":""}],"badges":[],"createdAt":"2025-05-31 08:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6789685/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6789685/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85537826,"identity":"c5627bb8-f7da-4d1c-a3db-bb0dedd7b8c6","added_by":"auto","created_at":"2025-06-27 05:39:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1329833,"visible":true,"origin":"","legend":"","description":"","filename":"FinalVersion1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6789685/v1_covered_04574776-f061-44ed-9a68-dc951bac996c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance Evaluation of Sustainable Concrete Incorporating Mineral Fillers: Experimental Insights and Predictive Modeling Using Advanced Machine Learning","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":"Quarry dust, Gene expression programming, Random Forest, Compressive strength, Flexural strength","lastPublishedDoi":"10.21203/rs.3.rs-6789685/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6789685/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Concrete production significantly contributes to environmental concerns due to its high demand for natural resources and carbon emissions. To address these issues, sustainable alternatives such as incorporating quarry dust as a partial replacement for fine aggregates have gained attention. However, optimizing such mixtures while ensuring desirable engineering properties remains challenging. This study investigates the use of quarry dust in concrete through experimental testing and advanced machine learning (ML) modeling to evaluate its performance as an eco-friendly alternative. An experimental program was conducted for two grades of concrete (M15 and M20) to assess the fresh and hardened properties of concrete incorporating quarry dust at replacement levels ranging from 0% to 60%, along with a superplasticizer. Key properties such as workability and compressive, tensile and flexural strength were measured to evaluate the concrete’s performance. 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