Explainable Machine Learning Based Optimization of Strength, Durability, and Carbon Efficiency of Fly Ash–GGBS Geopolymer Concrete

Explainable Machine Learning Based Optimization of Strength, Durability, and Carbon Efficiency of Fly Ash–GGBS 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 Article Explainable Machine Learning Based Optimization of Strength, Durability, and Carbon Efficiency of Fly Ash–GGBS Geopolymer Concrete Siva Shanmukha Anjaneya Babu Padavala, Deepak Kothuri, Abraham Mengistu Gashe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8915695/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Geopolymer concrete (GPC) is widely recognized as a sustainable alternative to ordinary Portland cement concrete; however, optimizing long-term durability, mechanical performance, and environmental impact remains a critical challenge. This study presents an integrated experimental, machine learning (ML), and life cycle assessment (LCA) framework to optimize the strength, durability, and carbon trade-off of fly ash (FA) and ground granulated blast furnace slag (GGBS) based GPC. Sixteen geopolymer mixtures were designed by systematically varying sodium hydroxide molarity (8–14 M), Si/Al ratio, FA:GGBS ratio (70:30 and 50:50), and curing regime (ambient curing and heat curing at 60 0 C). Compressive strength, rapid chloride penetration (RCP), water absorption, and sorptivity were evaluated at 28, 90, and 180 days to assess long-term performance. The results revealed continuous strength development and progressive durability enhancement with curing age, with an optimum NaOH molarity of 12 M identified for both mechanical and transport properties. GGBS rich mixtures exhibited superior long-term performance due to enhanced matrix densification, while heat curing significantly improved early-age strength and durability. The highest 180-day compressive strength of 75.1 MPa was obtained for the GPC-12-50-H mixture, whereas ambient-cured GPC-12-50-A demonstrated comparable long-term performance with improved sustainability. ML models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), were developed to predict compressive strength. XGBoost achieved the highest prediction accuracy (R² = 0.98), followed by RF (R² = 0.97). Explainable ML using SHAP analysis identified curing regime, NaOH molarity, and Si/Al ratio as the most influential parameters. Life cycle assessment showed increasing global warming potential and embodied energy with higher alkalinity, GGBS content, and heat curing. A carbon efficiency index based on 180 day strength identified a 12 M NaOH and 50:50 (FA:GGBS) ratio as the most sustainable mix design. Physical sciences/Engineering Physical sciences/Materials science Geopolymer concrete Explainable machine learning Durability performance Life cycle assessment Carbon efficiency index Sustainable construction materials Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Mar, 2026 Reviews received at journal 17 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviews received at journal 15 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers invited by journal 09 Mar, 2026 Editor assigned by journal 09 Mar, 2026 Editor invited by journal 09 Mar, 2026 Submission checks completed at journal 05 Mar, 2026 First submitted to journal 05 Mar, 2026 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-8915695","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":603388681,"identity":"bf856bdc-ac53-471d-83e3-87cbfabe3ec5","order_by":0,"name":"Siva Shanmukha Anjaneya Babu Padavala","email":"","orcid":"","institution":"Gudlavalleru Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Siva","middleName":"Shanmukha Anjaneya Babu","lastName":"Padavala","suffix":""},{"id":603388686,"identity":"284fccdc-1fcc-44e2-8f50-43737ec87865","order_by":1,"name":"Deepak Kothuri","email":"","orcid":"","institution":"Prasad V Potluri Siddhartha Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Deepak","middleName":"","lastName":"Kothuri","suffix":""},{"id":603388696,"identity":"a64b66f8-cd72-48f0-98c5-3b55c72caf3c","order_by":2,"name":"Abraham Mengistu Gashe","email":"data:image/png;base64,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","orcid":"","institution":"Addis Ababa Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Abraham","middleName":"Mengistu","lastName":"Gashe","suffix":""}],"badges":[],"createdAt":"2026-02-19 08:55:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8915695/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8915695/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-48502-6","type":"published","date":"2026-04-21T15:57:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":107929512,"identity":"40946493-083b-4bd0-84ce-35cc47121d2f","added_by":"auto","created_at":"2026-04-27 16:16:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1741496,"visible":true,"origin":"","legend":"","description":"","filename":"FAGGBSGPCSR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8915695/v1_covered_68be8252-fdd7-4478-b39f-1774a386f943.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Explainable Machine Learning Based Optimization of Strength, Durability, and Carbon Efficiency of Fly Ash–GGBS Geopolymer Concrete","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":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Geopolymer concrete, Explainable machine learning, Durability performance, Life cycle assessment, Carbon efficiency index, Sustainable construction materials","lastPublishedDoi":"10.21203/rs.3.rs-8915695/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8915695/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGeopolymer concrete (GPC) is widely recognized as a sustainable alternative to ordinary Portland cement concrete; however, optimizing long-term durability, mechanical performance, and environmental impact remains a critical challenge. This study presents an integrated experimental, machine learning (ML), and life cycle assessment (LCA) framework to optimize the strength, durability, and carbon trade-off of fly ash (FA) and ground granulated blast furnace slag (GGBS) based GPC. Sixteen geopolymer mixtures were designed by systematically varying sodium hydroxide molarity (8\u0026ndash;14 M), Si/Al ratio, FA:GGBS ratio (70:30 and 50:50), and curing regime (ambient curing and heat curing at 60\u003csup\u003e0\u003c/sup\u003eC). Compressive strength, rapid chloride penetration (RCP), water absorption, and sorptivity were evaluated at 28, 90, and 180 days to assess long-term performance. The results revealed continuous strength development and progressive durability enhancement with curing age, with an optimum NaOH molarity of 12 M identified for both mechanical and transport properties. GGBS rich mixtures exhibited superior long-term performance due to enhanced matrix densification, while heat curing significantly improved early-age strength and durability. The highest 180-day compressive strength of 75.1 MPa was obtained for the GPC-12-50-H mixture, whereas ambient-cured GPC-12-50-A demonstrated comparable long-term performance with improved sustainability. ML models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), were developed to predict compressive strength. XGBoost achieved the highest prediction accuracy (R\u0026sup2; = 0.98), followed by RF (R\u0026sup2; = 0.97). Explainable ML using SHAP analysis identified curing regime, NaOH molarity, and Si/Al ratio as the most influential parameters. Life cycle assessment showed increasing global warming potential and embodied energy with higher alkalinity, GGBS content, and heat curing. A carbon efficiency index based on 180 day strength identified a 12 M NaOH and 50:50 (FA:GGBS) ratio as the most sustainable mix design.\u003c/p\u003e","manuscriptTitle":"Explainable Machine Learning Based Optimization of Strength, Durability, and Carbon Efficiency of Fly Ash–GGBS Geopolymer Concrete","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 06:21:02","doi":"10.21203/rs.3.rs-8915695/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-27T02:49:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T11:00:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158237098862245980733953903831620617604","date":"2026-03-16T13:07:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-15T09:13:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263262838104065045249969121538649272513","date":"2026-03-09T20:56:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146227985563433991126612934840117806495","date":"2026-03-09T19:49:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-09T13:32:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-09T12:56:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-09T12:50:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-05T09:35:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-05T06:43:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"158b5ae9-0671-4e91-9799-acd7206900fb","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":64207453,"name":"Physical sciences/Engineering"},{"id":64207454,"name":"Physical sciences/Materials science"}],"tags":[],"updatedAt":"2026-04-27T16:16:52+00:00","versionOfRecord":{"articleIdentity":"rs-8915695","link":"https://doi.org/10.1038/s41598-026-48502-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-21 15:57:41","publishedOnDateReadable":"April 21st, 2026"},"versionCreatedAt":"2026-03-12 06:21:02","video":"","vorDoi":"10.1038/s41598-026-48502-6","vorDoiUrl":"https://doi.org/10.1038/s41598-026-48502-6","workflowStages":[]},"version":"v1","identity":"rs-8915695","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8915695","identity":"rs-8915695","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}