AI-Powered Surveillance for Intelligent Construction Site Management

AI-Powered Surveillance for Intelligent Construction Site Management | 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Article AI-Powered Surveillance for Intelligent Construction Site Management Varnit Dhanani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8726539/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 construction industry grapples with significant inefficiencies in equipment management, leading to project delays and cost overruns. Traditional manual monitoring methods are labor-intensive, prone to error, and fail to provide comprehensive coverage of large sites. This paper explores a practical AI-driven surveillance system that automates the tracking of construction equipment in real time. By leveraging advanced object detection models enhanced with a novel clustering technique, the system reliably identifies machinery even when small or partially obscured—a common challenge on active sites. This automation provides project managers with unprecedented visibility into asset utilization, enabling data-driven decisions to optimize resource allocation, reduce idle time, and improve operational efficiency. The proposed system represents a significant step towards intelligent construction site management, transforming raw video feeds into actionable business intelligence. Civil Engineering Construction Management Equipment Tracking Operational Efficiency AI Automation Resource Optimization Project Monitoring 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-8726539","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582139292,"identity":"ea4b8d46-4e22-425e-869a-bfd91c10af95","order_by":0,"name":"Varnit Dhanani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYNCDBAYbIMnYeIAItQZQLQlpIC0NJGhhSDgMpvBq4W/vPfbgQ80fBoPjZ589ePjjvN3a9sNAW2psonFpkThzLt1wxjEDBoMz6eYGCQm3k7edSQRqOZaW24DLRRI5ZtK8DQYMkg1pbBIgLWYHgFoYGw7j1/IXpKX/GUjLuWSz8w+J0MII1MIvAbblgJ3ZDQK2SJw5YybZc8yYh18CZEtacoLZDaAtCXj8wt/eYybxo0ZOjo0/jU3yh42dvdn59IfAMLTBqQUGeGCMRLDKBALKUYA9KYpHwSgYBaNgZAAA/lBaP3wR06oAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Varnit","middleName":"","lastName":"Dhanani","suffix":""}],"badges":[],"createdAt":"2026-01-29 03:25:38","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8726539/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8726539/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101469142,"identity":"03dc8f50-206e-4225-86bd-9cf7aafa9eee","added_by":"auto","created_at":"2026-01-30 04:41:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":173106,"visible":true,"origin":"","legend":"","description":"","filename":"AIPoweredSurveillanceforIntelligentConstructionSiteManagement2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8726539/v1_covered_c17ab584-05c4-418c-9ce8-9fb7056801bc.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAI-Powered Surveillance for Intelligent Construction Site Management\u003c/p\u003e","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":"Construction Management, Equipment Tracking, Operational Efficiency, AI Automation, Resource Optimization, Project Monitoring","lastPublishedDoi":"10.21203/rs.3.rs-8726539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8726539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe construction industry grapples with significant inefficiencies in equipment management, leading to project delays and cost overruns. 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