Predicting Sustainability Performance in Construction Projects Using

Predicting Sustainability Performance in Construction Projects Using | 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 Predicting Sustainability Performance in Construction Projects Using Ahmed Ali A. Shohan, Mohamed Alshayeb, Saleh Alsulamy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7664289/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 21 You are reading this latest preprint version Abstract The construction sector plays a major role in global environmental degradation, contributing significantly to carbon emissions, energy consumption, and waste generation. Despite the urgency to address these challenges, limited studies have explored the integrated prediction of sustainability performance using real-world project data, particularly in the context of Saudi Arabia. This study aims to bridge this gap by applying supervised machine learning techniques to predict carbon emissions and classify projects based on their emission levels. A structured survey was conducted, and 150 validated responses from key project stakeholders across Saudi Arabia were collected, covering a wide range of project and sustainability parameters. Three machine learning models, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB) were trained and evaluated. Using 10-fold cross-validation on the training set, XGB achieved the highest mean. In classification, both XGB and SVM achieved the highest accuracy of 76%, while RF followed with 73%. SHAP analysis revealed that waste generation, energy consumption, and project duration were the most influential predictors of carbon emissions. The findings offer a practical machine learning framework for early sustainability assessment and policy planning aligned with Saudi Vision 2030. Physical sciences/Energy science and technology Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Physical sciences/Mathematics and computing Carbon emissions prediction Machine learning Sustainability assessment SHAP interpretation Full Text Additional Declarations No competing interests reported. Supplementary Files CompleteData.rar ECM20243195.pdf Cite Share Download PDF Status: Published Journal Publication published 02 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 30 Oct, 2025 Reviews received at journal 29 Oct, 2025 Reviews received at journal 29 Oct, 2025 Reviews received at journal 29 Oct, 2025 Reviews received at journal 18 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviews received at journal 14 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers invited by journal 09 Oct, 2025 Editor assigned by journal 09 Oct, 2025 Editor invited by journal 09 Oct, 2025 Submission checks completed at journal 30 Sep, 2025 First submitted to journal 30 Sep, 2025 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-7664289","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":531772938,"identity":"def90ef7-940c-4654-8af4-8db77804775a","order_by":0,"name":"Ahmed Ali A. 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