Deep Learning for Prediction of Unconfined Compressive Strength Depending on Various Geotechnical Characteristics of some Soils of Boroughs of Sulaymaniyah City, Iraq Using H2O-ANN

Deep Learning for Prediction of Unconfined Compressive Strength Depending on Various Geotechnical Characteristics of some Soils of Boroughs of Sulaymaniyah City, Iraq Using H2O-ANN | 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 Deep Learning for Prediction of Unconfined Compressive Strength Depending on Various Geotechnical Characteristics of some Soils of Boroughs of Sulaymaniyah City, Iraq Using H2O-ANN Professor Nihad Bahaaldeen Salih, Peshawa Bakhtiar Salih, Tavga Aram Abdalla This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8809072/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 Geotechnical engineering faces significant challenges when dealing with fine-grained soils due to their high plasticity and, in some cases, swelling behavior. To address these challenges, this study aims to predict the Unconfined Compressive Strength (UCS) of various soil types collected from different districts of Sulaymaniyah City, Iraq, by developing an Artificial Neural Network (ANN) model using H2O’s deep learning framework. A comprehensive set of geotechnical properties including Atterberg limits, moisture content, density, specific gravity, and shear strength parameters was analyzed using 60 intact soil samples. The ANN model, developed using Python and the H2O-ANN library, successfully captured the complex nonlinear relationships between these input variables and UCS. The model achieved high predictive accuracy, with an R² value of 0.90 and low error metrics (RMSE ≈ 33.42 kPa and MAE ≈ 14.2 kPa). Model performance was further validated through residual analysis and partial dependence plots, which highlighted the significant influence of density, moisture content, and plasticity characteristics on UCS. Compared with conventional regression models and similar ANN-based studies on stabilized expansive soils, the proposed model demonstrates greater flexibility and improved prediction accuracy for natural, non-stabilized soils. This approach provides an efficient and practical alternative to time-consuming UCS laboratory testing and offers substantial benefits for geotechnical investigations in complex urban environments. The findings are particularly valuable for geotechnical analysis and design in construction projects, where ANN models can effectively represent critical soil behavior. Unconfined Compressive Strength Geotechnical Characteristics Soils Sulaymaniyah City H2O’s Deep Learning (H2O-ANN) 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-8809072","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588517391,"identity":"0e2094ac-9c7d-4350-9411-77e7e0df9f07","order_by":0,"name":"Professor Nihad Bahaaldeen Salih","email":"","orcid":"","institution":"University of Sulaymaniyah","correspondingAuthor":false,"prefix":"","firstName":"Professor","middleName":"Nihad Bahaaldeen","lastName":"Salih","suffix":""},{"id":588517393,"identity":"930d8926-45ba-40c7-ab19-0343541be570","order_by":1,"name":"Peshawa Bakhtiar Salih","email":"","orcid":"","institution":"University of Sulaymaniyah","correspondingAuthor":false,"prefix":"","firstName":"Peshawa","middleName":"Bakhtiar","lastName":"Salih","suffix":""},{"id":588517394,"identity":"7b37aa6d-7a0d-4928-bdae-314be659bae4","order_by":2,"name":"Tavga Aram Abdalla","email":"data:image/png;base64,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","orcid":"","institution":"University of Sulaymaniyah","correspondingAuthor":true,"prefix":"","firstName":"Tavga","middleName":"Aram","lastName":"Abdalla","suffix":""}],"badges":[],"createdAt":"2026-02-06 16:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8809072/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8809072/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107350537,"identity":"0a6adeab-13a0-41f2-8e88-9c8836475f9d","added_by":"auto","created_at":"2026-04-20 15:56:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1059431,"visible":true,"origin":"","legend":"","description":"","filename":"5ANNPaper25012026editedpeshawa.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8809072/v1_covered_a76ae44c-050d-4ef4-830d-d8fa64721ae7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning for Prediction of Unconfined Compressive Strength Depending on Various Geotechnical Characteristics of some Soils of Boroughs of Sulaymaniyah City, Iraq Using H2O-ANN","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":"Unconfined Compressive Strength, Geotechnical Characteristics, Soils, Sulaymaniyah City, H2O’s Deep Learning (H2O-ANN)","lastPublishedDoi":"10.21203/rs.3.rs-8809072/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8809072/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGeotechnical engineering faces significant challenges when dealing with fine-grained soils due to their high plasticity and, in some cases, swelling behavior. To address these challenges, this study aims to predict the Unconfined Compressive Strength (UCS) of various soil types collected from different districts of Sulaymaniyah City, Iraq, by developing an Artificial Neural Network (ANN) model using H2O’s deep learning framework. A comprehensive set of geotechnical properties including Atterberg limits, moisture content, density, specific gravity, and shear strength parameters was analyzed using 60 intact soil samples. The ANN model, developed using Python and the H2O-ANN library, successfully captured the complex nonlinear relationships between these input variables and UCS. The model achieved high predictive accuracy, with an R² value of 0.90 and low error metrics (RMSE ≈ 33.42 kPa and MAE ≈ 14.2 kPa). Model performance was further validated through residual analysis and partial dependence plots, which highlighted the significant influence of density, moisture content, and plasticity characteristics on UCS. 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