Reliability assessment of steel braces in seismically retrofitted buildings: A case study using a novel integral extrapolation scheme

Reliability assessment of steel braces in seismically retrofitted buildings: A case study using a novel integral extrapolation scheme | 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 Reliability assessment of steel braces in seismically retrofitted buildings: A case study using a novel integral extrapolation scheme Oleg Gaidai, Yasmin Ali, Ahmed Elgammal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7144556/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 The probabilistic assessment of structural reliability under extreme seismic loading is a critical challenge, compounded by the path-dependent nature of damage ('structural memory') and the numerical instability associated with extrapolating from limited, complex datasets. This paper introduces and formalizes a novel Logarithmic Integral Extrapolation Scheme designed to address these challenges. The methodology circumvents the direct fitting of complex distributions by first transforming the empirical failure probability data into a regularized integral domain, which is inherently more amenable to stable extrapolation. The utility and performance of the scheme are demonstrated through its application to a complex dataset of peak axial force demands generated from nonlinear time-history analyses of a six-story reinforced concrete building retrofitted with eccentric steel chevron braces and ductile shear links. The predictive capabilities of the proposed scheme are benchmarked against a conventional 4-parameter Weibull parametric fit. The results demonstrate that the logarithmic integration approach not only provides enhanced numerical stability but also yields a more conservative prediction for the design-level force demands. This enhanced conservatism provides a greater margin of safety for capacity-protected elements, establishing the proposed methodology as a robust and valuable tool for the reliability-based design and assessment of high-performance structural systems. Civil Engineering Computational Mathematics Applied Statistics Applied Mathematics nonlinear time-history analysis seismic reliability structural memory structural reliability tail extrapolation Full Text Additional Declarations The authors declare no competing interests. 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-7144556","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486624204,"identity":"dd8a2802-51f3-4250-886f-7f67a94e4cb2","order_by":0,"name":"Oleg Gaidai","email":"","orcid":"","institution":"College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China","correspondingAuthor":false,"prefix":"","firstName":"Oleg","middleName":"","lastName":"Gaidai","suffix":""},{"id":486624205,"identity":"373b5cae-3c55-494c-9712-347ea3e9a2bc","order_by":1,"name":"Yasmin Ali","email":"","orcid":"https://orcid.org/0000-0001-7413-0885","institution":"Department of Civil Engineering, Sichuan University, Chengdu 610065, China; 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