A Data-Driven Approach for Rapid Seismic Risk Prediction of RC Buildings

A Data-Driven Approach for Rapid Seismic Risk Prediction 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 A Data-Driven Approach for Rapid Seismic Risk Prediction of RC Buildings Mertcan YILMAZ, Tamer Dirikgil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8213624/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Feb, 2026 Read the published version in Bulletin of Earthquake Engineering → Version 1 posted 5 You are reading this latest preprint version Abstract In regions with high seismic activity, the rapid and accurate determination of vulnerable buildings within the existing building stock is a crucial step toward ensuring life safety by enabling preventive measures before future earthquakes. This study presents an artificial intelligence (AI)-based methodology for the rapid and reliable estimation of seismic risk levels in reinforced concrete (RC) frame structures. In this context, the existing building stock was simulated by incorporating parameters such as inadequate material strength, workmanship defects, and usage-related defects in load-bearing elements, which are commonly observed in structures. To establish a robust framework, nonlinear performance analyses were conducted in accordance with the seismic code, and seismic risk levels were classified based on performance outcomes. Through this simulation, 1,680 building profiles were generated, and their corresponding risk levels were determined. The resulting dataset was used to train and evaluate eight different machine learning (ML) models with hyperparameter optimization. Among these, the XGBoost model achieved the highest predictive accuracy of 92% on the test dataset. The results indicate that AI models can rapidly and accurately predict the seismic risk levels of RC buildings, providing a reliable basis for large-scale risk assessment and pre-earthquake decision-making. Rapid Seismic Risk Assessment Machine Learning AI Based Risk Prediction Evaluation of Building Stocks RC Building Performance Full Text Cite Share Download PDF Status: Published Journal Publication published 11 Feb, 2026 Read the published version in Bulletin of Earthquake Engineering → Version 1 posted Reviewers agreed at journal 05 Dec, 2025 Reviewers invited by journal 05 Dec, 2025 Editor invited by journal 30 Nov, 2025 Editor assigned by journal 28 Nov, 2025 First submitted to journal 27 Nov, 2025 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-8213624","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":555835327,"identity":"02317fea-43a8-426a-ac7f-80dd275abeb7","order_by":0,"name":"Mertcan YILMAZ","email":"","orcid":"","institution":"Erciyes University: Erciyes Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Mertcan","middleName":"","lastName":"YILMAZ","suffix":""},{"id":555835328,"identity":"511b0d1d-96ed-41c6-bc3a-afd6b6853648","order_by":1,"name":"Tamer 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