A Hybrid Physics-Informed Deep Learning Framework for Predictive Diagnostics of Concrete Fracture

A Hybrid Physics-Informed Deep Learning Framework for Predictive Diagnostics of Concrete Fracture | 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 A Hybrid Physics-Informed Deep Learning Framework for Predictive Diagnostics of Concrete Fracture Winsyahputra Ritonga, Ahmad Andi Solahuddin, Budiman Nasution, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8325015/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 This study presents a hybrid physics-informed deep learning framework for quantitative diagnostics of concrete cracks. Integrating finite element simulations with convolutional neural networks (CNNs), the model predicts key fracture mechanics parameters-Stress Intensity Factor ($K_I$) and Energy Release Rate (G-directly from stress-field images. Crucially, a custom loss function enforces the fundamental physical constraint $G=(\frac{K_I^2)}{E}$ during training, ensuring predictions are not only accurate but also physically consistent. Despite the constrained dataset ($n=50$), the model demonstrated stable convergence and successfully learned the underlying fracture patterns. Crucially, the enforced physical constraint $G=\frac{(K_I^2)}{E}$ proved highly effective as a powerful regularizer, which is evident in the controlled gap between the training and validation loss. The final test set evaluation yielded a Mean Absolute Error (MAE) of 3.7691. While this error is high due to data scarcity, the results successfully validate the framework's feasibility and physical fidelity, providing a trustworthy, first-principles-based tool for structural health monitoring beyond purely data-driven methods. concrete cracks physics-informed AI FEM fracture mechanics machine learning 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. 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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-8325015","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570528129,"identity":"927eb2de-bd5f-468a-8e1e-7ac6d6e8c6d2","order_by":0,"name":"Winsyahputra Ritonga","email":"","orcid":"","institution":"State University of Medan","correspondingAuthor":false,"prefix":"","firstName":"Winsyahputra","middleName":"","lastName":"Ritonga","suffix":""},{"id":570528130,"identity":"208223c6-8582-4f85-90f7-ee6ea52c1082","order_by":1,"name":"Ahmad Andi Solahuddin","email":"","orcid":"","institution":"State University of 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Fracture","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":"concrete cracks, physics-informed AI, FEM, fracture mechanics, machine 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Integrating finite element simulations with convolutional neural networks (CNNs), the model predicts key fracture mechanics parameters-Stress Intensity Factor ($K_I$) and Energy Release Rate (G-directly from stress-field images. Crucially, a custom loss function enforces the fundamental physical constraint $G=(\\frac{K_I^2)}{E}$ during training, ensuring predictions are not only accurate but also physically consistent. Despite the constrained dataset ($n=50$), the model demonstrated stable convergence and successfully learned the underlying fracture patterns. Crucially, the enforced physical constraint $G=\\frac{(K_I^2)}{E}$ proved highly effective as a powerful regularizer, which is evident in the controlled gap between the training and validation loss. The final test set evaluation yielded a Mean Absolute Error (MAE) of 3.7691. While this error is high due to data scarcity, the results successfully validate the framework's feasibility and physical fidelity, providing a trustworthy, first-principles-based tool for structural health monitoring beyond purely data-driven methods.","manuscriptTitle":"A Hybrid Physics-Informed Deep Learning Framework for Predictive Diagnostics of Concrete Fracture","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-08 04:46:53","doi":"10.21203/rs.3.rs-8325015/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"6b40b579-ad87-4804-b6f2-ad4e2b46be3c","owner":[],"postedDate":"January 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T08:39:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-08 04:46:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8325015","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8325015","identity":"rs-8325015","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}