Prediction on the composite foundation bearing capacity with GRA-SR-SVR

Prediction on the composite foundation bearing capacity with GRA-SR-SVR | 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 Prediction on the composite foundation bearing capacity with GRA-SR-SVR Lifei Dong, haiyu wei, Miao Wang, Bo Yu, Feiyu Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4443414/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 Composite foundation bearing capacity is the main basis of composite foundation design, and how to accurately predict the composite foundation bearing capacity is of great significance to foundation engineering. In order to analyze the main influencing factors of composite foundation bearing capacity, and predict the corresponding foundation bearing capacity. Based on the actual measurement data of vibration replacement stone column composite foundation project, the main factors affecting the bearing capacity of its foundation are identified by using gray correlation analysis (GRA) and stepwise regression (SR). On this basis, the support vector machine regression model (SVR) is constructed to predict the bearing capacity of the composite foundation. And the prediction results are compared with those of the BP neural network and GRA-SVR model. The results show that the main factors affecting the bearing capacity of vibration replacement stone column composite foundation include diameter, effective pile length, dense current, filling coefficient, natural density, replacement rate, bedding thickness, and pore ratio. The prediction accuracy of the GRA-SR-SVR, BP neural network, and GRA-SVR model are 98.23%, 97.08%, and 97.63% in order, and the GRA-SR-SVR model has the highest prediction accuracy, and it can accurately and effectively predict the composite foundation bearing capacity. composite foundation bearing capacity grey correlation analysis stepwise regression support vector machine prediction 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-4443414","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":314130066,"identity":"659d45df-a4d5-449d-9059-2a278e69a021","order_by":0,"name":"Lifei Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACPuYDBkDKhoeNvfnAgQ8/iNDCxpYA0pImx8dzLPHgzB7itRw2lpPwMT7MwUaUFuaNjwt+MSe2SfB8OMzAwyDPL3aAkBa2YuOZfWyJbdK9Gw4XWDAYzpydQECLfI+ZNG8PT2KbzNkNh2fwMCQY3CakhY0HpEUC6LCcB4d52IjVwvPDwJhNIoeBWC1Av/A2JMix8RwzAAayBGG/8INCjOfPfx759ubHHz78sJHnlyagBQwY2+BMCSKUg8EfYhWOglEwCkbBiAQA3EE+3DL5oCIAAAAASUVORK5CYII=","orcid":"","institution":"Chongqing Three Gorges University","correspondingAuthor":true,"prefix":"","firstName":"Lifei","middleName":"","lastName":"Dong","suffix":""},{"id":314130067,"identity":"bac62470-deb6-4829-98bf-858722db564c","order_by":1,"name":"haiyu wei","email":"","orcid":"","institution":"Chongqing Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"haiyu","middleName":"","lastName":"wei","suffix":""},{"id":314130069,"identity":"1e338e89-f3f0-40c6-a879-7324982526e7","order_by":2,"name":"Miao Wang","email":"","orcid":"","institution":"Chongqing Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Wang","suffix":""},{"id":314130070,"identity":"4612c94e-436b-4293-9f25-3a2bd7e430d6","order_by":3,"name":"Bo Yu","email":"","orcid":"","institution":"Chongqing Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Yu","suffix":""},{"id":314130072,"identity":"78461f93-a13a-4cbd-846a-894e3bf63901","order_by":4,"name":"Feiyu Chen","email":"","orcid":"","institution":"Chongqing Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Feiyu","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-05-19 07:57:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4443414/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4443414/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59914296,"identity":"61094907-5f3a-4915-b455-1123223f4056","added_by":"auto","created_at":"2024-07-09 08:40:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":247607,"visible":true,"origin":"","legend":"","description":"","filename":"PredictiononthecompositefoundationbearingcapacitywithGRASRSVR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4443414/v1_covered_87d2b616-bf5e-45c3-9dfb-9502618640dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction on the composite foundation bearing capacity with GRA-SR-SVR","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":"composite foundation bearing capacity, grey correlation analysis, stepwise regression, support vector machine, prediction","lastPublishedDoi":"10.21203/rs.3.rs-4443414/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4443414/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eComposite foundation bearing capacity is the main basis of composite foundation design, and how to accurately predict the composite foundation bearing capacity is of great significance to foundation engineering. In order to analyze the main influencing factors of composite foundation bearing capacity, and predict the corresponding foundation bearing capacity. Based on the actual measurement data of vibration replacement stone column composite foundation project, the main factors affecting the bearing capacity of its foundation are identified by using gray correlation analysis (GRA) and stepwise regression (SR). On this basis, the support vector machine regression model (SVR) is constructed to predict the bearing capacity of the composite foundation. And the prediction results are compared with those of the BP neural network and GRA-SVR model. The results show that the main factors affecting the bearing capacity of vibration replacement stone column composite foundation include diameter, effective pile length, dense current, filling coefficient, natural density, replacement rate, bedding thickness, and pore ratio. The prediction accuracy of the GRA-SR-SVR, BP neural network, and GRA-SVR model are 98.23%, 97.08%, and 97.63% in order, and the GRA-SR-SVR model has the highest prediction accuracy, and it can accurately and effectively predict the composite foundation bearing capacity.\u003c/p\u003e","manuscriptTitle":"Prediction on the composite foundation bearing capacity with GRA-SR-SVR","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-14 18:03:06","doi":"10.21203/rs.3.rs-4443414/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":"90d2e345-5f2c-4f17-b244-57a66c537390","owner":[],"postedDate":"June 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-09T08:32:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-14 18:03:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4443414","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4443414","identity":"rs-4443414","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}