Machine Learning Approaches for Analyzing and Predicting Concurrent Delays in Construction Projects

Machine Learning Approaches for Analyzing and Predicting Concurrent Delays in Construction 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 Research Article Machine Learning Approaches for Analyzing and Predicting Concurrent Delays in Construction Projects Sandamini Neththikumara, M W Pasan Maduranga, Nethshan Narasinghe, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6125612/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 Construction projects are highly complex and often vulnerable to delays due to multiple overlapping factors. This study employs machine learning techniques to analyse and predict concurrent delays, a critical issue in the construction industry. A synthetic dataset was generated to represent key factors influencing delays, including labour hours, resource availability, weather conditions, and project complexity. Preprocessing steps such as normalization and outlier handling were applied to ensure data quality. K-means clustering was used to identify patterns in delay occurrences, while a Random Forest classifier provided predictive insights. The model demonstrated robust performance, achieving high accuracy, precision, and recall in delay prediction. Comparative analysis with traditional methods highlighted the advantages of machine learning in identifying complex relationships between variables. This study provides actionable insights for stakeholders to mitigate delays and optimize project outcomes using data-driven approaches. Delay prediction Machine learning K-means clustering Random Forest Construction projects Project optimization 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. 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-6125612","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":422254143,"identity":"356571c7-1952-4fb2-95ed-ed8167d5b070","order_by":0,"name":"Sandamini Neththikumara","email":"data:image/png;base64,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","orcid":"","institution":"Häme University of Applied Sciences","correspondingAuthor":true,"prefix":"","firstName":"Sandamini","middleName":"","lastName":"Neththikumara","suffix":""},{"id":422254145,"identity":"4625d062-da85-4a4e-8986-b25cfeebbc15","order_by":1,"name":"M W Pasan Maduranga","email":"","orcid":"","institution":"University of Sri Jayewardenepura","correspondingAuthor":false,"prefix":"","firstName":"M","middleName":"W Pasan","lastName":"Maduranga","suffix":""},{"id":422254147,"identity":"3e039f36-1ed3-4885-94e9-a12901167e94","order_by":2,"name":"Nethshan Narasinghe","email":"","orcid":"","institution":"University of North Dakota","correspondingAuthor":false,"prefix":"","firstName":"Nethshan","middleName":"","lastName":"Narasinghe","suffix":""},{"id":422254148,"identity":"23c1fd20-9c6d-42ca-a05c-cab47b3cafae","order_by":3,"name":"Chandana Jayalath","email":"","orcid":"","institution":"University of Vocational Technology","correspondingAuthor":false,"prefix":"","firstName":"Chandana","middleName":"","lastName":"Jayalath","suffix":""}],"badges":[],"createdAt":"2025-02-28 05:53:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6125612/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6125612/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77635726,"identity":"ee51794b-1277-43cd-80a6-9703a1bae78f","added_by":"auto","created_at":"2025-03-03 18:31:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":468189,"visible":true,"origin":"","legend":"","description":"","filename":"submission.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6125612/v1_covered_56ca8887-a2f4-41a4-b66d-3b0a86060153.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning Approaches for Analyzing and Predicting Concurrent Delays in Construction Projects","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Delay prediction, Machine learning, K-means clustering, Random Forest, Construction projects, Project optimization","lastPublishedDoi":"10.21203/rs.3.rs-6125612/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6125612/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Construction projects are highly complex and often vulnerable to delays due to multiple overlapping factors. 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