Post-earthquake rapid seismic demand estimation at unmonitored locations via Bayesian networks

Post-earthquake rapid seismic demand estimation at unmonitored locations via Bayesian networks | 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 Post-earthquake rapid seismic demand estimation at unmonitored locations via Bayesian networks Pooria Mesbahi, Enrique García-Macías, Marco Breccolotti, Filippo Ubertini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4193333/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jul, 2024 Read the published version in Bulletin of Earthquake Engineering → Version 1 posted 5 You are reading this latest preprint version Abstract Post-earthquake safety assessment of buildings and infrastructure poses significant challenges, often relying on time-consuming visual inspections. To expedite this process, safety criteria based on a demand-capacity model are utilized. However, rapid assessment frameworks require accurate estimations of intensity measures (IMs) to estimate seismic demand and assess structural health. Unfortunately, post-earthquake IM values are typically only available at monitored locations equipped with sensors or monitoring systems, limiting broader assessments. Simple spatial interpolation methods, while possible, struggle to consider crucial physical factors such as earthquake magnitude, epicentral distance, and soil type, leading to substantial estimation errors, especially in areas with insufficient or non-uniform seismic station coverage. To address these issues, a novel framework, BN-GMPE, combining a Bayesian network (BN) and a ground motion prediction equation (GMPE), is proposed. BN-GMPE enables inference and prediction under uncertainty, incorporating physical parameters in seismic wave propagation. A further novelty introduced in this work regards the separation of the near and far seismic fields in the updating process to attain a clearer understanding of uncertainty and more accurate IM estimation. In the proposed approach, a GMPE is employed for the estimation, and the bias and standard deviation of the prediction error are updated after any new information is entered into the network. The proposed method is benchmarked against a classic Kriging interpolator technique, considering some recent earthquake shocks in Italy. The proposed BN framework can naturally extend for estimating the probability of failure of various structures in a targeted region, which represents the ultimate aim of this research. Bayesian networks Ground motion prediction equation Kriging surrogate model Machine learning Markov chain Monte Carlo Seismic demand model Full Text Cite Share Download PDF Status: Published Journal Publication published 24 Jul, 2024 Read the published version in Bulletin of Earthquake Engineering → Version 1 posted Editorial decision: Minor revisions needed 23 Jun, 2024 Reviewers agreed at journal 08 May, 2024 Reviewers invited by journal 01 Apr, 2024 Editor assigned by journal 31 Mar, 2024 First submitted to journal 30 Mar, 2024 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-4193333","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286143522,"identity":"4443d8c9-d846-4c6c-ba72-0d9ff9aa953e","order_by":0,"name":"Pooria Mesbahi","email":"","orcid":"","institution":"University of Perugia: Universita degli Studi di Perugia","correspondingAuthor":false,"prefix":"","firstName":"Pooria","middleName":"","lastName":"Mesbahi","suffix":""},{"id":286143523,"identity":"8ed60e7b-f191-4f25-abc5-c4dfbc6cf1d7","order_by":1,"name":"Enrique García-Macías","email":"","orcid":"","institution":"University of Granada: Universidad de Granada","correspondingAuthor":false,"prefix":"","firstName":"Enrique","middleName":"","lastName":"García-Macías","suffix":""},{"id":286143524,"identity":"4f6b1395-84c4-4d31-b4d4-33a00e816dc6","order_by":2,"name":"Marco Breccolotti","email":"","orcid":"","institution":"University of Perugia: Universita degli Studi di Perugia","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"","lastName":"Breccolotti","suffix":""},{"id":286143525,"identity":"d0bdb361-8808-4d2f-9a75-9d2356423082","order_by":3,"name":"Filippo Ubertini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIie3RsWrDMBAG4BMevFzqVYHQZxAIOgX8KoVAJjd1t4wKhmQpne036ahy4MkPYIi3QqYOBS+leOjZJCQElHYMVP8gCUkfp7MBfHyuMGEWGDusZD8owIinfgddBEmcEMtkbPbEZdAKA0fSF7KHIxcJxYqeXqegtlnZfqbNRNcPb5R+wyR2kUAYKqo5qKac5Vbt8K5e3FPx4n5Y3JPRmkDVieZeiEmiaPR8oZcjeWwHovO/kyQYiJJM8OsXwr3guCk1VHxZVh+KCiMRrYNERC1/sdubbfYOy47iaJPoNu2mcWgcZThc6OwnBGIt3ff35GwHuovCx8fH53/lB8IDWwCHrgddAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5044-8482","institution":"Universita degli Studi di Perugia","correspondingAuthor":true,"prefix":"","firstName":"Filippo","middleName":"","lastName":"Ubertini","suffix":""}],"badges":[],"createdAt":"2024-03-30 19:04:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4193333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4193333/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10518-024-01979-w","type":"published","date":"2024-07-24T16:15:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61597084,"identity":"e7d90dea-7895-4832-9538-ca32ac3442bd","added_by":"auto","created_at":"2024-08-01 17:31:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4091785,"visible":true,"origin":"","legend":"","description":"","filename":"MainPaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4193333/v1_covered_f4da7527-2569-4d49-81aa-982009ca47b0.pdf"}],"financialInterests":"","formattedTitle":"Post-earthquake rapid seismic demand estimation at unmonitored locations via Bayesian networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Bayesian networks, Ground motion prediction equation, Kriging surrogate model, Machine learning, Markov chain Monte Carlo, Seismic demand model","lastPublishedDoi":"10.21203/rs.3.rs-4193333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4193333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Post-earthquake safety assessment of buildings and infrastructure poses significant challenges, often relying on time-consuming visual inspections. 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