A Novel Approach to Predicting Liquefaction-Induced Settlements Using Kolmogorov-Arnold Networks (KANs)

A Novel Approach to Predicting Liquefaction-Induced Settlements Using Kolmogorov-Arnold Networks (KANs) | 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 A Novel Approach to Predicting Liquefaction-Induced Settlements Using Kolmogorov-Arnold Networks (KANs) Seyidcem Karakaş This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4590072/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract This study investigates the applicability and effectiveness of Kolmogorov-Arnold Networks (KAN) in predicting settlements due to soil liquefaction, a critical issue in geotechnical engineering. Soil liquefaction, resulting from increased pore water pressure, diminishes soil bearing capacity and can lead to significant structural damage. Utilizing a comprehensive dataset derived from field and laboratory studies, the data was divided into training (70%), validation (15%), and testing (15%) sets and processed as torch tensors for the KAN model. The model, consisting of three layers with grid and k parameters set to 3 and 11, respectively, was trained using the LBFGS optimizer and MSE Loss function over 125 steps. The KAN model demonstrated superior performance with an R² value of 0.935 and an MAE of 0.14 on the training set, and an R² of 0.908 and an MAE of 0.176 on the test set. Comparative analysis with other studies showed that KAN outperformed traditional neural network models. Feature importance analysis revealed “unit_weight” as the most significant feature, aligning with previous studies. These results underscore the potential of KAN in enhancing predictive accuracy and reliability in geotechnical applications, paving the way for its broader acceptance and implementation in real-world scenarios. Kolmogorov-Arnold Networks Liguefaction-Induced Settlement Artificial Intelligence Neural Networks Geotechnical Engineering Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Jul, 2024 Reviews received at journal 23 Jul, 2024 Reviews received at journal 20 Jul, 2024 Reviewers agreed at journal 16 Jul, 2024 Reviews received at journal 16 Jul, 2024 Reviewers agreed at journal 13 Jul, 2024 Reviewers agreed at journal 13 Jul, 2024 Reviewers agreed at journal 13 Jul, 2024 Reviewers invited by journal 13 Jul, 2024 Editor assigned by journal 11 Jul, 2024 Submission checks completed at journal 10 Jul, 2024 First submitted to journal 16 Jun, 2024 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. 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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-4590072","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":331629410,"identity":"d404630d-520d-47f3-b22f-45f45a47e705","order_by":0,"name":"Seyidcem Karakaş","email":"data:image/png;base64,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","orcid":"","institution":"Istanbul Technical University","correspondingAuthor":true,"prefix":"","firstName":"Seyidcem","middleName":"","lastName":"Karakaş","suffix":""}],"badges":[],"createdAt":"2024-06-16 14:24:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4590072/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4590072/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61740713,"identity":"3d9c4137-3b0c-4864-ab1f-62f8a6d33188","added_by":"auto","created_at":"2024-08-05 05:06:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":451776,"visible":true,"origin":"","legend":"","description":"","filename":"001makale.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4590072/v1_covered_9065266f-4a54-43a7-8f3e-3ee41aa08157.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Approach to Predicting Liquefaction-Induced Settlements Using Kolmogorov-Arnold Networks (KANs)","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Kolmogorov-Arnold Networks, Liguefaction-Induced Settlement, Artificial Intelligence, Neural Networks, Geotechnical Engineering","lastPublishedDoi":"10.21203/rs.3.rs-4590072/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4590072/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the applicability and effectiveness of Kolmogorov-Arnold Networks (KAN) in predicting settlements due to soil liquefaction, a critical issue in geotechnical engineering. Soil liquefaction, resulting from increased pore water pressure, diminishes soil bearing capacity and can lead to significant structural damage. Utilizing a comprehensive dataset derived from field and laboratory studies, the data was divided into training (70%), validation (15%), and testing (15%) sets and processed as torch tensors for the KAN model. The model, consisting of three layers with grid and k parameters set to 3 and 11, respectively, was trained using the LBFGS optimizer and MSE Loss function over 125 steps. The KAN model demonstrated superior performance with an R\u0026sup2; value of 0.935 and an MAE of 0.14 on the training set, and an R\u0026sup2; of 0.908 and an MAE of 0.176 on the test set. Comparative analysis with other studies showed that KAN outperformed traditional neural network models. Feature importance analysis revealed \u0026ldquo;unit_weight\u0026rdquo; as the most significant feature, aligning with previous studies. 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