Ensemble Learning for Predicting Concrete Compressive Strength

Ensemble Learning for Predicting Concrete Compressive Strength | 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 Ensemble Learning for Predicting Concrete Compressive Strength Hiba Al-Taie, Hamid Mukhtar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7288832/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 Machine learning techniques, particularly ensemble learning, are transforming the construction industry by significantly enhancing the accuracy of concrete compressive strength predictions. This study explores various ensemble learning methods to predict compressive strength using two distinct datasets: a real-world dataset from Advanced Construction Technology Services (ACTS) and a laboratory dataset from the UCI Machine Learning repository. We evaluated multiple ensemble algorithms, including Random Forest, Histogram-based Gradient Boosting, LightGBM, XGBoost, CatBoost, and Stacking, whose performance was assessed using mean squared error (MSE), root mean square error (RMSE), and the coefficient of determination ($R^2$). The results revealed that for the field dataset, Random Forest, CatBoost, and Stacking achieved the best predictive performance, while for the laboratory dataset, Stacking and CatBoost were the top performing models. To further enhance predictive accuracy, one of the best performing algorithms was selected for each dataset and optimized using random search and nested cross-validation to ensure robust model validation. Additionally, SHapley Additive Explanations (SHAP) were used to interpret the importance of features and analyze the influence of different input variables on the predictions of compressive strength. Our findings indicate that models trained on laboratory data generally outperformed those trained on field data. The optimized models achieved the best overall performance, reinforcing the value of model optimization in ensemble learning and offering practical insights to improve the reliability of concrete compressive strength predictions in the construction industry. Physical sciences/Engineering Physical sciences/Materials science Physical sciences/Mathematics and computing 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-7288832","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":502993923,"identity":"51ce3853-600b-40f9-b06e-f0b7715de906","order_by":0,"name":"Hiba Al-Taie","email":"","orcid":"","institution":"University of Birmingham Dubai","correspondingAuthor":false,"prefix":"","firstName":"Hiba","middleName":"","lastName":"Al-Taie","suffix":""},{"id":502993924,"identity":"58ed9bad-1a60-4f78-9d50-c8119dbdad0a","order_by":1,"name":"Hamid Mukhtar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACAyBmbGwAkuw9bDBBZmK0ACmeMyRrkcghUos5A3ea5Mwdf+TNZ7499uBnDoM8fwOPsQE+LZYNvNskN54xMJxzOy/dsHcbg+GMAzzGCXgddgCo5WGbAeMM6RwzCd5tDIwbGHiMDxCjxX6G5Bkzyb/bGOyJ07KxzSBxhgSPmTTQlkSQFvwOO8y72XJmm3HyDJ68NGnZbRLJMw6zFeP1vsHx3o03e9vkbGewnz0m+XabjW1/e/NmCXxa0ONAglCsjIJRMApGwSggBgAANcFDwP0TBa8AAAAASUVORK5CYII=","orcid":"","institution":"University of Birmingham Dubai","correspondingAuthor":true,"prefix":"","firstName":"Hamid","middleName":"","lastName":"Mukhtar","suffix":""}],"badges":[],"createdAt":"2025-08-04 08:38:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7288832/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7288832/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92247517,"identity":"c39697c6-f81d-453b-b8fc-76fbdcc5830f","added_by":"auto","created_at":"2025-09-26 09:54:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2455768,"visible":true,"origin":"","legend":"","description":"","filename":"SubmissiontoScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7288832/v1_covered_2423e0f1-3e9e-4a5a-bfe4-a5681d03a9fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ensemble Learning for Predicting Concrete Compressive Strength","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":"","lastPublishedDoi":"10.21203/rs.3.rs-7288832/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7288832/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Machine learning techniques, particularly ensemble learning, are transforming the construction industry by significantly enhancing the accuracy of concrete compressive strength predictions. This study explores various ensemble learning methods to predict compressive strength using two distinct datasets: a real-world dataset from Advanced Construction Technology Services (ACTS) and a laboratory dataset from the UCI Machine Learning repository. We evaluated multiple ensemble algorithms, including Random Forest, Histogram-based Gradient Boosting, LightGBM, XGBoost, CatBoost, and Stacking, whose performance was assessed using mean squared error (MSE), root mean square error (RMSE), and the coefficient of determination ($R^2$). The results revealed that for the field dataset, Random Forest, CatBoost, and Stacking achieved the best predictive performance, while for the laboratory dataset, Stacking and CatBoost were the top performing models. To further enhance predictive accuracy, one of the best performing algorithms was selected for each dataset and optimized using random search and nested cross-validation to ensure robust model validation. Additionally, SHapley Additive Explanations (SHAP) were used to interpret the importance of features and analyze the influence of different input variables on the predictions of compressive strength. Our findings indicate that models trained on laboratory data generally outperformed those trained on field data. The optimized models achieved the best overall performance, reinforcing the value of model optimization in ensemble learning and offering practical insights to improve the reliability of concrete compressive strength predictions in the construction industry. 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