Explainable Machine Learning for Non-Destructive Prediction of Hollow Concrete Block Strength: A Comparative and Interpretive Study

Explainable Machine Learning for Non-Destructive Prediction of Hollow Concrete Block Strength: A Comparative and Interpretive Study | 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 Explainable Machine Learning for Non-Destructive Prediction of Hollow Concrete Block Strength: A Comparative and Interpretive Study Maaz Khan, Muhammad Faisal Javed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6935591/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 This study formulates a sophisticated prediction model based on machine learning algorithms Deep Neural Networks (DNN) and Gene Expression Programming (GEP) to predict the compressive strength (CS) of hollow concrete masonry prisms. A total of 159 experimental samples gathered from literature were utilized, including important input parameters: mortar strength (fm), block strength (fb), height-to-thickness ratio (h/t), and ratio fm/fb. The DNN model, which was trained and tested, exhibited outstanding predictive accuracy with an R² of 0.9998, well surpassing the GEP model, which offered a more interpretable but slightly less accurate mathematical expression. To provide transparency and interpretability of the black-box DNN model, Explainable Artificial Intelligence (XAI) methods such as Shapley Additive Explanations (SHAP), Individual Conditional Expectation (ICE), and Sensitivity Analysis (SA) were used. The resulting analyses repeatedly nominated fb as the strongest predictor for CS, trailed by h/t and fm. The research ensures the efficacy of using machine learning in conjunction with XAI for enhancing the prediction accuracy and the interpretability in structural material inspection. The result has real-world implications for ensuring optimized block construction design, decreased use of destructive testing, as well as fostering sustainable and resilient building. 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-6935591","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489404431,"identity":"4855678c-0a99-4af5-9d65-5059baad9a63","order_by":0,"name":"Maaz Khan","email":"","orcid":"","institution":"Ghulam Ishaq Khan Institute of Engineering Sciences and Technology","correspondingAuthor":false,"prefix":"","firstName":"Maaz","middleName":"","lastName":"Khan","suffix":""},{"id":489404432,"identity":"c6594ee4-4fa3-4ca6-b3c3-4250ca0cc045","order_by":1,"name":"Muhammad Faisal Javed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIie2RsUoDQRCGZzlImolpF+7wXmFECIJiXmWPha0OOQiEiIXKwVqmDfgUNpL2ONBmtT5IY3oLgxYHWrgrIjYbYye4H8zwF/vBPyxAIPAnQYjgwG6Izh+AhA1AdsQPinIKK+lXioVp/vlyvdK/vKueC3GYpBeVPn4pVALd8pqzufIq/P5IxjMhkUymFwnlCHgz5szk/l4GKe61ERJYhdMEgecDzvTEa6QGd19RnGI6XerRh5I+rlfI4CBGUSM0mWZPrhhHp/iL7ZgttT8Tt0jNsoyBFHZQjfYy4z9/2/TqRSFOhulUVqv2TQ773fqqWc2l//zvRO4fOy5lZ5sZwNqvtKkSCAQC/4F3LclMRAeK804AAAAASUVORK5CYII=","orcid":"","institution":"Ghulam Ishaq Khan Institute of Engineering Sciences and Technology","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Faisal","lastName":"Javed","suffix":""}],"badges":[],"createdAt":"2025-06-20 05:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6935591/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6935591/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91617749,"identity":"2f147c1c-fced-4dc6-aca5-fa1b2fd46b46","added_by":"auto","created_at":"2025-09-18 10:54:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1363027,"visible":true,"origin":"","legend":"","description":"","filename":"MaazKhan0443.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6935591/v1_covered_91f2e8a9-030d-4196-af95-c2163862d36e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Explainable Machine Learning for Non-Destructive Prediction of Hollow Concrete Block Strength: A Comparative and Interpretive Study","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-6935591/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6935591/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study formulates a sophisticated prediction model based on machine learning algorithms Deep Neural Networks (DNN) and Gene Expression Programming (GEP) to predict the compressive strength (CS) of hollow concrete masonry prisms. 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