Non-Destructive Concrete Strength Prediction Using AI: A Comparative Study of Machine Learning and Deep Learning Models

Non-Destructive Concrete Strength Prediction Using AI: A Comparative Study of Machine Learning and Deep Learning Models | 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 Non-Destructive Concrete Strength Prediction Using AI: A Comparative Study of Machine Learning and Deep Learning Models Nima Ekhteraey, Milad Ekhteraei, Mohammad Amir Sattari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6462021/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 Accurate prediction of concrete's mechanical properties is a crucial aspect of civil engineering, ensuring the structural integrity and durability of constructions. Traditional destructive testing methods, while reliable, are time-consuming and resource-intensive. This study presents a novel, non-destructive approach for predicting compressive, tensile, and flexural strengths of concrete using only two input parameters: Ultrasonic Pulse Velocity (UPV) and Electrical Resistivity (ER). A comparative analysis was conducted utilizing five machine learning and deep learning models: Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Convolutional Neural Networks (CNN). The results demonstrated that CNN outperformed all other models, achieving the lowest Root Mean Square Error (RMSE) and Mean Relative Error (MRE) across all three concrete strength predictions. Specifically, CNN achieved an MRE of 1.37% for compressive strength, 1.25% for tensile strength, and 1.76% for flexural strength, highlighting its superior predictive accuracy compared to traditional machine learning models. CNN's strong performance stems from its ability to learn deep, non-linear feature hierarchies from minimal inputs. By capturing complex spatial and functional dependencies between UPV and ER, CNN can model the intricate mechanical behavior of concrete more effectively than shallow models. This makes it particularly suitable for tasks involving highly non-linear physical phenomena, such as predicting strength characteristics from indirect measurements. This research highlights the potential of AI-driven non-destructive testing as an efficient alternative to traditional methods, offering significant advantages in terms of cost reduction, speed, and sustainability in the construction industry. Physical sciences/Engineering/Civil engineering Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering/Mechanical engineering Non-Destructive Testing Concrete Strength Prediction Deep Learning Machine Learning Convolutional Neural Networks Ultrasonic Pulse Velocity Electrical Resistivity 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. 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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-6462021","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":452562988,"identity":"711f2f73-8319-4e12-9f7c-a8a30e11f4c4","order_by":0,"name":"Nima Ekhteraey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDACHh4og70BSBhYEKWFsQHCOADSIkGKFokEMElYBz/P2eMPPu6xkzO4+fzqhh8FEgz87d0JeLVI9vYlNs54lmxscDun7GYP0GESZ85uwKvF4DyPYTPPAebEDbdz0m7wALUYSOTi12IP0vLnQH39hptn0m7+IUaLAW+PYTPDgcMJBjfYj90myhaJM2cMZ/YcOG4480wO220ZAwkegn7h78kx+PDjQLU83/Hjz26++WMjx9/ei18LHCgc4DEA0TyEFCKAfAP7A+JVj4JRMApGwYgCALWHTFQ921a2AAAAAElFTkSuQmCC","orcid":"","institution":"Razi University","correspondingAuthor":true,"prefix":"","firstName":"Nima","middleName":"","lastName":"Ekhteraey","suffix":""},{"id":452562989,"identity":"fd86afdd-7649-4934-8701-e2c490097f51","order_by":1,"name":"Milad Ekhteraei","email":"","orcid":"","institution":"Kermanshah University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Milad","middleName":"","lastName":"Ekhteraei","suffix":""},{"id":452562990,"identity":"85f1cace-2f0b-45c6-b561-6a58050f01d4","order_by":2,"name":"Mohammad Amir Sattari","email":"","orcid":"","institution":"Razi University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Amir","lastName":"Sattari","suffix":""}],"badges":[],"createdAt":"2025-04-16 09:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6462021/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6462021/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83443207,"identity":"655f8a77-b025-4429-964c-ea5d9abaf9b0","added_by":"auto","created_at":"2025-05-26 10:17:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3868429,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6462021/v1_covered_2439259f-72ae-49bd-8de6-143ece67202b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Non-Destructive Concrete Strength Prediction Using AI: A Comparative Study of Machine Learning and Deep Learning Models","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":"Non-Destructive Testing, Concrete Strength Prediction, Deep Learning, Machine Learning, Convolutional Neural Networks, Ultrasonic Pulse Velocity, Electrical Resistivity","lastPublishedDoi":"10.21203/rs.3.rs-6462021/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6462021/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate prediction of concrete's mechanical properties is a crucial aspect of civil engineering, ensuring the structural integrity and durability of constructions. Traditional destructive testing methods, while reliable, are time-consuming and resource-intensive. This study presents a novel, non-destructive approach for predicting compressive, tensile, and flexural strengths of concrete using only two input parameters: Ultrasonic Pulse Velocity (UPV) and Electrical Resistivity (ER). A comparative analysis was conducted utilizing five machine learning and deep learning models: Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Convolutional Neural Networks (CNN). The results demonstrated that CNN outperformed all other models, achieving the lowest Root Mean Square Error (RMSE) and Mean Relative Error (MRE) across all three concrete strength predictions. 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