Physics-Anchored Sequence Learning for Predicting Hysteresis of RC Shear Walls

Physics-Anchored Sequence Learning for Predicting Hysteresis of RC Shear Walls | 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 Physics-Anchored Sequence Learning for Predicting Hysteresis of RC Shear Walls Shuai Zhang, Xue-xu An, Jing-jing Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8716044/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract We propose a physics-anchored sequence model for predicting force–displac ement hysteresis of reinforced concrete shear walls under reversed cyclic loading. A degrading Bouc–Wen model is first identified for each specimen using robust fitting and displacement sub-stepping, providing interpretable priors for stiffness, strength scale, and degradation. A recurrent network then predicts the latent hysteretic state and reconstructs restoring force, trained with a data loss and discrete Bouc–Wen constraints, and further regularized by tangent-stiffness and per-cycle energy consistency. We evaluate the method on 21 open-access shear-wall tests with heterogeneous loading protocols. On seven held-out specimens, the predictor achieves a mean \(\:{R}^{2}\) realizes around 0.97 (median around 0.99); errors concentrate near reversals and at the largest drift cycles. The learned latent state and identified parameters enable direct inspection of stiffness deterioration and strength loss, offering both predictive accuracy and interpretable diagnostics. Reinforced concrete shear walls Hysteresis modeling and prediction Physics-informed neural networks Tangent stiffness and energy dissipation Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 15 Feb, 2026 Reviewers invited by journal 13 Feb, 2026 Editor invited by journal 12 Feb, 2026 Editor assigned by journal 07 Feb, 2026 First submitted to journal 06 Feb, 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. 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-8716044","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591003754,"identity":"3b683c0a-2e2d-47b3-8484-140b3c716121","order_by":0,"name":"Shuai Zhang","email":"","orcid":"","institution":"Xi'an University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Zhang","suffix":""},{"id":591003755,"identity":"c54e7304-d863-4a65-aa51-1243d4c95e03","order_by":1,"name":"Xue-xu An","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIie3QsWrDMBCAYQmBvFzdVUbgvIKKxxj6KhIBTxkyhW6JK1Cm7M6UVygUOjsYmsXJbOgSU8ic4j1UmVvkZMugH245+Dg4hHy+O+yR4PwgX1KggS4JonZV9pBoobU41Vkcwqe8joh6a6KVqZKYjcV1BDXKcKCZMmj8002mKA4bibuJQ+BC6SFAasnunRd7lESNJLxwEMJU/gXMXsHLD/JgkHprJCXgIJSpOQdRKUPgeCGzXgKweY0KWSWUAr0QKfoIC3L75DKLrUg47NnTqm41d5HnKmgP6pzCYP3ddjBNB+F2tOlc5M9VO3h+A/D5fD7ff/0C8vVLE8Ix9NsAAAAASUVORK5CYII=","orcid":"","institution":"shaanxi college of communication technology","correspondingAuthor":true,"prefix":"","firstName":"Xue-xu","middleName":"","lastName":"An","suffix":""},{"id":591003756,"identity":"ce754393-9c00-40dd-8465-d499bf8e9432","order_by":2,"name":"Jing-jing Li","email":"","orcid":"","institution":"shaanxi college of communications technology","correspondingAuthor":false,"prefix":"","firstName":"Jing-jing","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-01-28 04:01:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8716044/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8716044/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103049598,"identity":"3d946f92-e6fd-4492-80e3-73bdb5b29297","added_by":"auto","created_at":"2026-02-20 07:43:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4428841,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8716044/v1_covered_43ca547d-d6ad-41af-8906-1410564aa812.pdf"}],"financialInterests":"","formattedTitle":"Physics-Anchored Sequence Learning for Predicting Hysteresis of RC Shear Walls","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bulletin-of-earthquake-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"beee","sideBox":"Learn more about [Bulletin of Earthquake Engineering](https://www.springer.com/journal/10518)","snPcode":"10518","submissionUrl":"https://submission.nature.com/new-submission/10518/3","title":"Bulletin of Earthquake Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Reinforced concrete shear walls, Hysteresis modeling and prediction, Physics-informed neural networks, Tangent stiffness and energy dissipation","lastPublishedDoi":"10.21203/rs.3.rs-8716044/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8716044/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe propose a physics-anchored sequence model for predicting force\u0026ndash;displac ement hysteresis of reinforced concrete shear walls under reversed cyclic loading. A degrading Bouc\u0026ndash;Wen model is first identified for each specimen using robust fitting and displacement sub-stepping, providing interpretable priors for stiffness, strength scale, and degradation. A recurrent network then predicts the latent hysteretic state and reconstructs restoring force, trained with a data loss and discrete Bouc\u0026ndash;Wen constraints, and further regularized by tangent-stiffness and per-cycle energy consistency. We evaluate the method on 21 open-access shear-wall tests with heterogeneous loading protocols. On seven held-out specimens, the predictor achieves a mean \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}\\)\u003c/span\u003e\u003c/span\u003e realizes around 0.97 (median around 0.99); errors concentrate near reversals and at the largest drift cycles. 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