Multi-Objective Adaptive Experimental Approach for Optimizing 3D Concrete Printing Mixtures and Parameters Incorporating Construction and Demolition Waste for Sustainable Construction

Multi-Objective Adaptive Experimental Approach for Optimizing 3D Concrete Printing Mixtures and Parameters Incorporating Construction and Demolition Waste 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 Multi-Objective Adaptive Experimental Approach for Optimizing 3D Concrete Printing Mixtures and Parameters Incorporating Construction and Demolition Waste for Sustainable Construction Thomas Tawiah Baah, Hee-Jeong Kim, Marat Latypov This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6891507/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract 3D concrete printing (3DCP) is emerging as a transformative technology in the construction industry with a potential for lower manufacturing costs, enhanced design flexibility, and greater efficiency. Integrating construction and demolition waste (CDW) into 3DCP mixtures offers sustainability benefits but at the same time is associated with challenges of material variability and complex printing requirements. This study introduces a multi-objective Bayesian approach to concurrent optimization of mixture design and printing parameters targeting superior buildability with maximized CDW replacement. Following experimental trials driven by the multi-objective Bayesian optimization algorithm, we achieved 66 % improvement in terms of buildability at 97 % CDW replacement of natural sand. These mixture designs together with optimized printing parameters allowed 3DCP of six to ten layers without collapse. Mechanical tests combined with XRD and SEM characterization showed that higher CDW content with silica fume increased compressive strength, particularly in cast specimens. The tests performed in multiple directions further revealed anisotropy of compressive strength in 3D-printed samples with the highest strength in the Y-direction followed by that in the X and Z directions. Our findings demonstrate a viable path toward sustainable, high-performance concrete printing with substantial use of recycled materials facilitated by multiobjective Bayesian optimization approaches. 3D Concrete printing Construction demolition waste Sustainable construction Multi-objective Bayesian optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Jul, 2025 Reviews received at journal 07 Jul, 2025 Reviews received at journal 03 Jul, 2025 Reviews received at journal 02 Jul, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers invited by journal 26 Jun, 2025 Editor assigned by journal 19 Jun, 2025 Submission checks completed at journal 19 Jun, 2025 First submitted to journal 13 Jun, 2025 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. 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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-6891507","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":478093966,"identity":"1ec8c603-5ed9-4c57-b5a4-950af4e07e1e","order_by":0,"name":"Thomas Tawiah Baah","email":"","orcid":"","institution":"University of Arizona","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"Tawiah","lastName":"Baah","suffix":""},{"id":478093971,"identity":"a34ae70c-3077-4e81-a92e-c0f46b86af43","order_by":1,"name":"Hee-Jeong Kim","email":"","orcid":"","institution":"University of Arizona","correspondingAuthor":false,"prefix":"","firstName":"Hee-Jeong","middleName":"","lastName":"Kim","suffix":""},{"id":478093972,"identity":"ef6783c9-5949-490f-ab0c-3a1edc8fc8bd","order_by":2,"name":"Marat Latypov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACPhiDH4gPQNkGeLWwMTCDaQnJBpK1GBxACBLQwn7+6Iafe+zqjI+ffXjwR8UdeQb25m0SeLXwJLPd7HmWLGF2Jt3ggMSZZ4YNPMfK8GthSGa7wXOAWcLsQBrDAcO2w4wNEjlm+LXwP2a7+edAvYRx/zOGA4lth+0b5N8Q0CKRzHab58BhCQMJoC0H2w4nNkjwENLy2Oy2zIHjkjNuPGM42HDmcHIbT1qxBT4t/PyJz26+OVDNz9+fxvzxR8Vh2372wxtv4NOCxV7SlI+CUTAKRsEowAYAj55JWcWxx7UAAAAASUVORK5CYII=","orcid":"","institution":"University of Arizona","correspondingAuthor":true,"prefix":"","firstName":"Marat","middleName":"","lastName":"Latypov","suffix":""}],"badges":[],"createdAt":"2025-06-14 03:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6891507/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6891507/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85716828,"identity":"b5e9b587-79d6-42a6-ab92-21015ac943f0","added_by":"auto","created_at":"2025-07-01 04:15:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12467075,"visible":true,"origin":"","legend":"","description":"","filename":"3dcpsubmissionrevised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6891507/v1_covered_9b50fcee-e922-4e4b-a9a8-85ab80e34a15.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Objective Adaptive Experimental Approach for Optimizing 3D Concrete Printing Mixtures and Parameters Incorporating Construction and Demolition Waste for Sustainable Construction","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"discover-concrete-and-cement","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Discover Concrete and Cement","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"Unsupported Journal","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"3D Concrete printing, Construction demolition waste, Sustainable construction, Multi-objective Bayesian optimization","lastPublishedDoi":"10.21203/rs.3.rs-6891507/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6891507/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e3D concrete printing (3DCP) is emerging as a transformative technology in the construction industry with a potential for lower manufacturing costs, enhanced design flexibility, and greater efficiency. Integrating construction and demolition waste (CDW) into 3DCP mixtures offers sustainability benefits but at the same time is associated with challenges of material variability and complex printing requirements. This study introduces a multi-objective Bayesian approach to concurrent optimization of mixture design and printing parameters targeting superior buildability with maximized CDW replacement. Following experimental trials driven by the multi-objective Bayesian optimization algorithm, we achieved 66 % improvement in terms of buildability at 97 % CDW replacement of natural sand. These mixture designs together with optimized printing parameters allowed 3DCP of six to ten layers without collapse. Mechanical tests combined with XRD and SEM characterization showed that higher CDW content with silica fume increased compressive strength, particularly in cast specimens. 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