Explainable machine learning predictive model for unconfined compressive strength of micron material stabilized soil

Explainable machine learning predictive model for unconfined compressive strength of micron material stabilized soil | 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 predictive model for unconfined compressive strength of micron material stabilized soil Zhihua Zhu, Zihang He, Zulong Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7621953/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 The traditional soil reinforcement technique using micron materials mixed with cement is limited in its widespread application in road engineering due to high costs. At the same time, the carbon dioxide released during cement production has significant negative impacts on the environment. To address these issues, this study proposes an innovative solution that uses micron-sized silica powder, alumina powder, and iron powder from industrial waste materials as modifiers for soil reinforcement. This solution is expected to reduce the amount of cement used and enhance the unconfined compressive strength (UCS) of the soil. Firstly, through laboratory experiments, this study tested the effect of different mixing ratios of micron-sized silica powder, alumina powder, and iron powder on the UCS of the soil and established a corresponding experimental database. Secondly, to predict the UCS of micron material-stabilized soil, this study used six machine learning (ML) methods optimized by random search (RS) to build UCS prediction models and comprehensively evaluated their prediction performance using four evaluation indicators. Finally, the results show that the eXtreme Gradient Boosting (XGB) model performs excellently on four performance metrics and can accurately predict the UCS of micron material-stabilized soil, with a coefficient of determination of 0.95 on the test set. This study provides reliable database and ML model support for the design and construction of stabilized soil in road engineering. Micron material stabilized soil Unconfined compressive strength Database Machine learning eXtreme Gradient Boosting model 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-7621953","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521273606,"identity":"8350590d-86a5-4c6e-9c20-2cd595eadde1","order_by":0,"name":"Zhihua 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soil","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":"Micron material stabilized soil, Unconfined compressive strength, Database, Machine learning, eXtreme Gradient 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At the same time, the carbon dioxide released during cement production has significant negative impacts on the environment. To address these issues, this study proposes an innovative solution that uses micron-sized silica powder, alumina powder, and iron powder from industrial waste materials as modifiers for soil reinforcement. This solution is expected to reduce the amount of cement used and enhance the unconfined compressive strength (UCS) of the soil. Firstly, through laboratory experiments, this study tested the effect of different mixing ratios of micron-sized silica powder, alumina powder, and iron powder on the UCS of the soil and established a corresponding experimental database. Secondly, to predict the UCS of micron material-stabilized soil, this study used six machine learning (ML) methods optimized by random search (RS) to build UCS prediction models and comprehensively evaluated their prediction performance using four evaluation indicators. Finally, the results show that the eXtreme Gradient Boosting (XGB) model performs excellently on four performance metrics and can accurately predict the UCS of micron material-stabilized soil, with a coefficient of determination of 0.95 on the test set. 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