PINN-CHK: Physics-informed neural network for high-fidelity prediction of early-age cement hydration kinetics

PINN-CHK: Physics-informed neural network for high-fidelity prediction of early-age cement hydration kinetics | 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 PINN-CHK: Physics-informed neural network for high-fidelity prediction of early-age cement hydration kinetics Md Asif Rahman, Tianjie Zhang, Yang Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3450308/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Apr, 2024 Read the published version in Neural Computing and Applications → Version 1 posted You are reading this latest preprint version Abstract Cement hydration kinetics, characterized by heat generation in early concrete stages, poses a modeling challenge. This study introduces the Physics-Informed Neural Network for Cement Hydration Kinetics (PINN-CHK) to investigate early-age temperature rises in cement paste. PINN-CHK leverages data-driven solutions to craft a high-fidelity prediction model, encompassing material properties and maturity functions in cement hydration. Trained on heated cement paste data, it simultaneously fits experimental results and underlying physics, yielding a mesh-free simulation. Incorporating governing partial differential equations, initial and boundary conditions into its loss function, PINN-CHK architecture undergoes rigorous benchmark testing, demonstrating unparalleled predictive accuracy compared to conventional deep learning methods. It excels in forecasting complete temperature fields during spatial-temporal cement hydration, achieving a remarkable relative L2 error as low as 0.00341. PINN-CHK achieves exceptional convergence and accuracy with only 5% of the training data, ushering in a new era in this crucial field. This innovative approach bridges the gap between theory and practice, offering an attractive alternative to conventional finite element solvers for enhanced comprehension of cement hydration kinetics and concrete maturity and strength development in cement-based materials. Civil Engineering Physics-informed neural network Deep learning Cement hydration kinetics Hydration temperature Concrete maturity and strength Full Text Cite Share Download PDF Status: Published Journal Publication published 25 Apr, 2024 Read the published version in Neural Computing and Applications → 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. 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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-3450308","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":240324176,"identity":"b0cc31fc-03c2-4f0c-8b20-c5b83ce4a6db","order_by":0,"name":"Md Asif Rahman","email":"","orcid":"https://orcid.org/0000-0003-4742-9763","institution":"Boise State University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Md","middleName":"Asif","lastName":"Rahman","suffix":""},{"id":240324177,"identity":"dcea5098-06f0-446b-bb0e-2d271bec2c2c","order_by":1,"name":"Tianjie Zhang","email":"","orcid":"https://orcid.org/0000-0002-6697-0777","institution":"Boise State University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tianjie","middleName":"","lastName":"Zhang","suffix":""},{"id":240324175,"identity":"db99e447-446d-4447-9e40-96a757d7bd96","order_by":2,"name":"Yang Lu","email":"data:image/png;base64,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","orcid":"","institution":"Boise State University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2023-10-16 02:45:45","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-3450308/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3450308/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00521-024-09791-y","type":"published","date":"2024-04-26T00:30:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55410141,"identity":"1e4a5f52-cb76-4686-8884-31fa31c0a53a","added_by":"auto","created_at":"2024-04-27 00:30:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1632043,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3450308/v1_covered_8cc74a5c-5e72-4e37-81df-b033147e5b93.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003ePINN-CHK: Physics-informed neural network for high-fidelity prediction of early-age cement hydration kinetics\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Boise State University","isAcceptedByJournal":true,"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":"Physics-informed neural network, Deep learning, Cement hydration kinetics, Hydration temperature, Concrete maturity and strength","lastPublishedDoi":"10.21203/rs.3.rs-3450308/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3450308/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCement hydration kinetics, characterized by heat generation in early concrete stages, poses a modeling challenge. This study introduces the Physics-Informed Neural Network for Cement Hydration Kinetics (PINN-CHK) to investigate early-age temperature rises in cement paste. PINN-CHK leverages data-driven solutions to craft a high-fidelity prediction model, encompassing material properties and maturity functions in cement hydration. Trained on heated cement paste data, it simultaneously fits experimental results and underlying physics, yielding a mesh-free simulation. Incorporating governing partial differential equations, initial and boundary conditions into its loss function, PINN-CHK architecture undergoes rigorous benchmark testing, demonstrating unparalleled predictive accuracy compared to conventional deep learning methods. It excels in forecasting complete temperature fields during spatial-temporal cement hydration, achieving a remarkable relative L2 error as low as 0.00341. 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