AI-Enhanced Data-Driven Approach to Model the Mechanical Behavior of Sustainable Geopolymer Concrete

AI-Enhanced Data-Driven Approach to Model the Mechanical Behavior of Sustainable Geopolymer Concrete | 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 AI-Enhanced Data-Driven Approach to Model the Mechanical Behavior of Sustainable Geopolymer Concrete Rajani Gautam, Rishav Jaiswal, Uday Shankar Yadav This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5307352/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 increasing environmental concerns associated with Ordinary Portland Cement (OPC) production have driven research towards alternative, sustainable construction materials. Geopolymer concrete (GPC) has emerged as a promising eco-friendly substitute, offering reduced carbon emissions and improved mechanical properties. However, accurately predicting the compressive strength of GPC remains a complex task due to the numerous variables influencing its performance, such as material properties, mix proportions, and curing conditions. This study develops an interpretable machine learning (ML) model to predict the compressive strength of geopolymer concrete, leveraging various ML techniques, including linear regression, decision trees (DT), gradient boosting, support vector regression (SVR), artificial neural networks (ANN), and random forests (RF). To enhance prediction accuracy, a super learner model is employed, integrating these individual techniques. The model's performance is evaluated using metrics such as the coefficient of determination (R²), mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE). Additionally, SHAP values and sensitivity analysis are conducted to quantify the impact of each input parameter on the predictions, ensuring the model's transparency and reliability. The proposed approach provides a robust framework for accurately forecasting the compressive strength of geopolymer concrete, thereby contributing to the advancement of sustainable construction practices. sustainable construction geopolymer concrete artificial intelligence machine learning SHAP sensitivity analysis Full Text Additional Declarations No competing interests reported. Supplementary Files Modelcomparision.xlsx 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-5307352","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369187288,"identity":"b206825a-c0a4-41ad-9dee-4c8a89b3e599","order_by":0,"name":"Rajani Gautam","email":"","orcid":"","institution":"Pokhara University Bhalwari","correspondingAuthor":false,"prefix":"","firstName":"Rajani","middleName":"","lastName":"Gautam","suffix":""},{"id":369187289,"identity":"122210a9-2f2a-4454-b51e-d21830b0f8e1","order_by":1,"name":"Rishav Jaiswal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACxgbGBiBlI0eyljRjCDeBeJ2HExuI1sLcfrj5M+8e5vS17acTP/78YZPPwH74AX6H9SS2SfM8Y8vddiZ3szRPQpplA0+aAX4tDYltzDkHeHK33eDdIM2QcNiAQYKBgJb+h82fcw5IpJvd4N3880fCf6AW9g/4tcxIbJDOOWCQANSyTYIn4QBQCw8BW2Y8bJP+cyDBEOiXbdY8ackGbDw5BXi1GPanP/4448B/ebPjZzff/GFjZ8DPfnwDfi0N6CJseNUDgTwhBaNgFIyCUTAKGACTYke4rsFeUQAAAABJRU5ErkJggg==","orcid":"","institution":"McMaster University","correspondingAuthor":true,"prefix":"","firstName":"Rishav","middleName":"","lastName":"Jaiswal","suffix":""},{"id":369187290,"identity":"13c73ba2-5176-483d-8353-40b286ba3f64","order_by":2,"name":"Uday Shankar Yadav","email":"","orcid":"","institution":"Indian Institute of Technology Patna Bihta","correspondingAuthor":false,"prefix":"","firstName":"Uday","middleName":"Shankar","lastName":"Yadav","suffix":""}],"badges":[],"createdAt":"2024-10-22 00:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5307352/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5307352/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69283676,"identity":"5fb8d493-d044-4d09-be80-86ee9dfc5506","added_by":"auto","created_at":"2024-11-18 19:15:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3514287,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5307352/v1_covered_3c5eb715-f705-4d14-97ea-d7f0d3b4777e.pdf"},{"id":68172757,"identity":"f78238a9-83be-4a29-8a6e-8c9c478613e1","added_by":"auto","created_at":"2024-11-04 10:43:30","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14285,"visible":true,"origin":"","legend":"","description":"","filename":"Modelcomparision.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5307352/v1/210f70ee07ebb07917c0864b.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI-Enhanced Data-Driven Approach to Model the Mechanical Behavior of Sustainable Geopolymer Concrete","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":"sustainable construction, geopolymer concrete, artificial intelligence, machine learning, SHAP, sensitivity analysis","lastPublishedDoi":"10.21203/rs.3.rs-5307352/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5307352/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing environmental concerns associated with Ordinary Portland Cement (OPC) production have driven research towards alternative, sustainable construction materials. 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