Physics-Informed Machine Learning Framework for Post-Earthquake Damage Classification of RC Buildings

Physics-Informed Machine Learning Framework for Post-Earthquake Damage Classification of RC Buildings | 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-Informed Machine Learning Framework for Post-Earthquake Damage Classification of RC Buildings Fatma Cansin Cinel, Fatih Sutcu, Gulsen Taskin Kaya, De-Cheng Feng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8958748/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 physics-informed machine learning framework for post-earthquake damage classification using building-level survey data collected after the 2023 Kahramanmaras earthquake sequence. To ensure structural homogeneity and avoid typology-driven biases common in prior studies, the analysis focuses exclusively on reinforced-concrete (RC) residential buildings. The feature set combines directly used parameters, such as shear-wave velocity to represent site conditions, with physics-informed engineered features, including standardized spectral accelerations, modified coordinates, and a combined energy metric. These features are designed to capture spatial shaking variability and structural vulnerability more effectively than raw survey attributes. Several supervised models spanning tree-ensemble, kernel-based, and neural architectures are trained and evaluated using a nested cross-validation scheme that yields unbiased generalization estimates and ensures that learning-based steps such as scaling and SMOTE are fitted only on the training folds and then applied to the validation/test folds, preventing information leakage. Severe class imbalance across Slight, Moderate, and Heavy damage states is addressed through Borderline-SMOTE oversampling and class-weighted learning. Model performance is evaluated using class-balanced metrics, including macro-F1, balanced accuracy, and G-Mean, to ensure fair assessment under class imbalance. In addition, SHAP-based analyses are conducted to offer a transparent interpretation of feature contributions. The results show that physics-informed feature engineering, combined with systematic validation and imbalance handling, substantially improves the reliability of post-earthquake damage classification for RC residential buildings. Earthquake damage assessment Reinforced concrete buildings Feature engineering Physics-informed machine learning SHAP analysis Kahramanmaras Earthquake Sequence (2023) Full Text 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8958748","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604141382,"identity":"46cb0500-7a0e-4d2c-afff-01dbe7288c6b","order_by":0,"name":"Fatma Cansin Cinel","email":"","orcid":"","institution":"Istanbul Technical University - Ayazaga Campus: Istanbul Teknik Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Fatma","middleName":"Cansin","lastName":"Cinel","suffix":""},{"id":604141383,"identity":"8ed132e5-cdee-462e-b8d1-13ec3d442eae","order_by":1,"name":"Fatih Sutcu","email":"","orcid":"","institution":"Istanbul Technical University - Ayazaga Campus: Istanbul Teknik Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Fatih","middleName":"","lastName":"Sutcu","suffix":""},{"id":604141384,"identity":"ae654daf-317e-4782-9b45-5b4bdb45ff08","order_by":2,"name":"Gulsen Taskin Kaya","email":"","orcid":"","institution":"Istanbul Technical University - Ayazaga Campus: Istanbul Teknik Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Gulsen","middleName":"Taskin","lastName":"Kaya","suffix":""},{"id":604141385,"identity":"45e6b6f4-7f61-4a41-8e36-8e1bd1bf4fbc","order_by":3,"name":"De-Cheng Feng","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"De-Cheng","middleName":"","lastName":"Feng","suffix":""},{"id":604141386,"identity":"6c5b4a83-3d0e-4d0a-b773-c33a881f7e00","order_by":4,"name":"ZEYNEP TUNA DEGER","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYJADxgcJYDqBOOUSQMxskJAA1XKASC1sEgzEaJFv7zF78HEHQ53B7eZnFQ9/HGbgZ88xYP64B7cWxp4z5oYzzzBIGNw5ZnYjIeEwg2TPGwOGA89wa2GWyDGT5m0DarmRANFicCMHqAWPy9jk35hJ/wVrSf9WANJiT0gLjwSPmTQjWEuOGQPYFgkCWiR40soke89ISM68kVMskZCWziNx5lnBgTN4tMi3H94m8XOHDT/fjfSNH3/YWMvxtydvfFCBRwsDA4cBA2ODBMKlIAKvBgYG9gdALfiVjIJRMApGwQgHALB0UP2yWY8dAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3585-6477","institution":"Istanbul Technical University - Ayazaga Campus: Istanbul Teknik Universitesi","correspondingAuthor":true,"prefix":"","firstName":"ZEYNEP","middleName":"TUNA","lastName":"DEGER","suffix":""}],"badges":[],"createdAt":"2026-02-24 14:42:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8958748/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8958748/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107870685,"identity":"495a1692-7941-4f67-8c8f-018ec60b2f4d","added_by":"auto","created_at":"2026-04-27 07:40:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":37001971,"visible":true,"origin":"","legend":"","description":"","filename":"KahramanmarasmakaleBEE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8958748/v1_covered_9c48939e-8a47-4d23-8a0c-1e585325ec45.pdf"}],"financialInterests":"","formattedTitle":"Physics-Informed Machine Learning Framework for Post-Earthquake Damage Classification of RC Buildings","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":"Earthquake damage assessment, Reinforced concrete buildings, Feature engineering, Physics-informed machine learning, SHAP analysis, Kahramanmaras Earthquake Sequence (2023)","lastPublishedDoi":"10.21203/rs.3.rs-8958748/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8958748/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study presents a physics-informed machine learning framework for post-earthquake damage classification using building-level survey data collected after the 2023 Kahramanmaras earthquake sequence. 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