Digital Twin Technology for Cost Optimization and Risk Reduction in Infrastructure Projects

Digital Twin Technology for Cost Optimization and Risk Reduction in Infrastructure Projects | 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 Article Digital Twin Technology for Cost Optimization and Risk Reduction in Infrastructure Projects Nelyufar Umarovna Dadabayeva This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8294991/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 Digital Twin (DT) technology is increasingly recognized as a strategic tool for improving cost efficiency and risk management in infrastructure projects through real-time data integration, predictive analytics, and lifecycle optimization. As global infrastructure systems continue to experience cost overruns, resource inefficiencies, and elevated risk exposure, DT provides a dynamic digital–physical interface that enhances transparency, operational accuracy, and decision-making across all project phases. This study examines the impact of DT adoption on cost optimization and risk reduction through multi-level integration with Building Information Modeling (BIM), the Internet of Things (IoT), and Artificial Intelligence (AI). A mixed-method research design was applied, combining a systematic literature review, comparative case analysis, and quantitative modeling based on 12 international infrastructure projects implemented between 2020 and 2024 in the transport, energy, and civil engineering sectors. The results indicate that advanced DT implementation can reduce lifecycle costs by up to 25% and operational risk exposure by approximately 30%, depending on digital maturity, interoperability, and data governance quality. Projects utilizing predictive analytics and sensor-based monitoring also demonstrated notable improvements in maintenance efficiency, safety performance, and environmental outcomes. The study proposes an empirical DT cost–risk evaluation framework tailored to developing economies, offering practical guidance for policymakers and infrastructure managers in assessing digital investment efficiency and sustainability outcomes. Digital Twin Infrastructure Projects Cost Optimization Risk Reduction Smart Construction BIM Predictive Maintenance Infrastructure Management Predictive Analytics Full Text Additional Declarations No competing interests reported. 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. 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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-8294991","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":570445617,"identity":"d78d951d-50a7-47f4-a825-e075b025e553","order_by":0,"name":"Nelyufar Umarovna Dadabayeva","email":"data:image/png;base64,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","orcid":"","institution":"Tashkent State Transport University","correspondingAuthor":true,"prefix":"","firstName":"Nelyufar","middleName":"Umarovna","lastName":"Dadabayeva","suffix":""}],"badges":[],"createdAt":"2025-12-06 13:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8294991/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8294991/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99799214,"identity":"638ccb61-5f3b-4a2f-a51f-cd5b615f1f5d","added_by":"auto","created_at":"2026-01-08 13:49:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":854195,"visible":true,"origin":"","legend":"","description":"","filename":"N.U.Dadabayeva2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8294991/v1_covered_4d5d2472-3b20-43d0-a4aa-c34f3a1392ff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digital Twin Technology for Cost Optimization and Risk Reduction in Infrastructure Projects","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Digital Twin, Infrastructure Projects, Cost Optimization, Risk Reduction, Smart Construction, BIM, Predictive Maintenance, Infrastructure Management, Predictive Analytics","lastPublishedDoi":"10.21203/rs.3.rs-8294991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8294991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDigital Twin (DT) technology is increasingly recognized as a strategic tool for improving cost efficiency and risk management in infrastructure projects through real-time data integration, predictive analytics, and lifecycle optimization. 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