Hybrid Machine Learning Approach for Accurate Bearing Capacity Prediction of Geogrid-Reinforced Soils: Integrating ANN and Optimization Algorithms

Hybrid Machine Learning Approach for Accurate Bearing Capacity Prediction of Geogrid-Reinforced Soils: Integrating ANN and Optimization Algorithms | 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 Hybrid Machine Learning Approach for Accurate Bearing Capacity Prediction of Geogrid-Reinforced Soils: Integrating ANN and Optimization Algorithms Ippili Saikrishnamacharyulu, Nagarampalli Manoj Kumar, Prakash Ranjan sahoo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7270729/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 In geotechnical engineering, identifying the bearing capacity of soil is a crucial and fundamental aspect. The strength and stability of any structure depend significantly on this bearing capacity, and it has a direct and considerable impact. There are many traditional estimation methods and formulas available to determine bearing capacity. Often, traditional methods don’t work well when dealing with complicated soil conditions because soil behavior can change a lot depending on various factors. That’s why using hybrid learning techniques can be really helpful. These advanced methods provide us a better chance of predicting the bearing capacity of soil more accurately. In this approach, a dataset of one thousand entries is taken, which is then divided into training and testing sets. In this study, we take into account several key factors such as cohesion (c), internal friction angle (φ), unit weight (γ), footing width (B), foundation depth (D), and whether the load is static or dynamic. To improve the accuracy of estimating the soil bearing capacity, here we make use of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). These hybrid, intelligent models have performed well not just in how they function but also in terms of their simplicity and the reliability of their results. In recent years, there has been growing interest in using artificial intelligence (AI) to estimate the bearing capacity of soils. Although these techniques do come with some limitations, they are particularly effective when it comes to working with complex or large datasets. A major strength of AI methods is their ability to Identify hidden patterns and Connections that traditional calculation methods often overlook. Bearing Capacity Hybrid Machine Learning Artificial Neural Networks (ANN) Genetic Algorithm (GA) Particle Swarm Optimization (PSO) Geogrid-Reinforced Soil Shallow Foundations 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-7270729","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498319791,"identity":"edf61358-4e28-4a8d-9518-c5b3718cefbc","order_by":0,"name":"Ippili Saikrishnamacharyulu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYFACxmYGBgOGBDD7A4MEmJYgWgvjDKBiHsJaGJhBBFgLM0g5QS3y7YebjSsK6vLM2Q8/+2zzy0LOnoH54G0ePFoMziQ2J54xOFxs2ZNmPDu3T8KYh4Et2RqvFobE5oMNBgcSNxxIMGbO7ZFI7GHgMZPGp0W+/yFIS13ihvPPPzNb9kjU9zDwf8OrheEG0GENBsyJG27kGDMz/JBI4GHgYcOrxeDGw2bDBoPDQC1vihl7GyQMew6zGVvOweuw9MeSDX9ADkvfzPDjT508e3vzwxtv8DkMBTC2MUDjiXjwhzTlo2AUjIJRMDIAAO7iSzgXRfNIAAAAAElFTkSuQmCC","orcid":"","institution":"Gandhi Institute of Engineering and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Ippili","middleName":"","lastName":"Saikrishnamacharyulu","suffix":""},{"id":498319792,"identity":"12b77340-f123-4dad-a8e5-d548f4b92af8","order_by":1,"name":"Nagarampalli Manoj Kumar","email":"","orcid":"","institution":"Gandhi Institute of Engineering and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Nagarampalli","middleName":"Manoj","lastName":"Kumar","suffix":""},{"id":498319793,"identity":"fff0cbbb-0fa3-47d9-a2b2-d86c27cbbb1f","order_by":2,"name":"Prakash Ranjan sahoo","email":"","orcid":"","institution":"Gandhi Institute of Engineering and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Prakash","middleName":"Ranjan","lastName":"sahoo","suffix":""}],"badges":[],"createdAt":"2025-08-01 11:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7270729/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7270729/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89553552,"identity":"be844690-81f7-4e5c-97ef-1e406cc7357a","added_by":"auto","created_at":"2025-08-21 08:53:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":632897,"visible":true,"origin":"","legend":"","description":"","filename":"HybridMachineLearningApproachforAccurateBearingCapacityPredictionofGeogridReinforcedSoilsIntegratingANNandOptimizationAlgorithms.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7270729/v1_covered_16977384-e95a-4b75-9de6-aa3f6c2cdd9b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid Machine Learning Approach for Accurate Bearing Capacity Prediction of Geogrid-Reinforced Soils: Integrating ANN and Optimization Algorithms","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":"Bearing Capacity, Hybrid Machine Learning, Artificial Neural Networks (ANN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Geogrid-Reinforced Soil, Shallow Foundations","lastPublishedDoi":"10.21203/rs.3.rs-7270729/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7270729/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn geotechnical engineering, identifying the bearing capacity of soil is a crucial and fundamental aspect. The strength and stability of any structure depend significantly on this bearing capacity, and it has a direct and considerable impact. There are many traditional estimation methods and formulas available to determine bearing capacity. Often, traditional methods don\u0026rsquo;t work well when dealing with complicated soil conditions because soil behavior can change a lot depending on various factors. That\u0026rsquo;s why using hybrid learning techniques can be really helpful. These advanced methods provide us a better chance of predicting the bearing capacity of soil more accurately. In this approach, a dataset of one thousand entries is taken, which is then divided into training and testing sets. In this study, we take into account several key factors such as cohesion (c), internal friction angle (φ), unit weight (γ), footing width (B), foundation depth (D), and whether the load is static or dynamic. To improve the accuracy of estimating the soil bearing capacity, here we make use of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). These hybrid, intelligent models have performed well not just in how they function but also in terms of their simplicity and the reliability of their results. In recent years, there has been growing interest in using artificial intelligence (AI) to estimate the bearing capacity of soils. Although these techniques do come with some limitations, they are particularly effective when it comes to working with complex or large datasets. A major strength of AI methods is their ability to Identify hidden patterns and Connections that traditional calculation methods often overlook.\u003c/p\u003e","manuscriptTitle":"Hybrid Machine Learning Approach for Accurate Bearing Capacity Prediction of Geogrid-Reinforced Soils: Integrating ANN and Optimization Algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 03:17:03","doi":"10.21203/rs.3.rs-7270729/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"3a2a7af6-2e0c-4033-8123-0fd29042d741","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-21T08:53:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-13 03:17:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7270729","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7270729","identity":"rs-7270729","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}