A Hybrid SSA-CNN-SVM Model for Seismic-Induced Sand Liquefaction Discrimination

A Hybrid SSA-CNN-SVM Model for Seismic-Induced Sand Liquefaction Discrimination | 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 Hybrid SSA-CNN-SVM Model for Seismic-Induced Sand Liquefaction Discrimination Ying Yuan, Yunming Su, Mingyu Zhao, Aihong Zhou, Lei Ren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7188692/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Feb, 2026 Read the published version in Bulletin of Earthquake Engineering → Version 1 posted 5 You are reading this latest preprint version Abstract Seismic-induced sand liquefaction‌ represents ‌a high-impact geohazard‌, ‌rendering‌ the discrimination‌ and ‌prediction‌ of ‌sand liquefaction states‌ ‌essential‌ for ‌geohazard mitigation research‌. For the rational discrimination of sand liquefaction states‌, this study proposes an SSA-CNN-SVM model that integrates Sparrow Search Algorithm (SSA)-optimized Convolutional Neural Networks (CNN) with Support Vector Machines (SVM) for liquefaction discrimination. This model initiates from raw sand liquefaction data, accomplishes layer-by-layer learning to extract liquefaction features and discriminate the states of liquefaction, and employs SVM in lieu of Softmax functions for liquefaction state classification. Taking the sand liquefaction case from the Tangshan earthquake as the comprehensive dataset, the evaluation metrics - standard penetration test (SPT) blow count, mean particle size, coefficient of uniformity, groundwater table depth, effective overburden pressure, seismic intensity, and cyclic shear stress ratio - are input into the SSA-CNN-SVM model for prediction. The results are compared with those from SSA-SVM, SVM, CNN, and Backpropagation Neural Network (BPNN) models, validated against actual sand liquefaction data. The results indicate that the SSA-CNN-SVM model demonstrates superior performance in sand liquefaction discrimination, achieving an accuracy of 83.33%, precision of 83.33%, recall of 83.33%, and F1-Score of 83.33% – all exceeding corresponding metrics of other comparative models. This validates the high precision of the proposed liquefaction discrimination model and provides a novel approach for practical applications. ‌sand liquefaction discrimination model convolutional neural network support vector machine sparrow search algorithm Full Text Cite Share Download PDF Status: Published Journal Publication published 04 Feb, 2026 Read the published version in Bulletin of Earthquake Engineering → Version 1 posted Reviewers agreed at journal 28 Aug, 2025 Reviewers invited by journal 28 Aug, 2025 Editor invited by journal 01 Aug, 2025 Editor assigned by journal 29 Jul, 2025 First submitted to journal 25 Jul, 2025 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-7188692","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":507044108,"identity":"7391f205-07e3-40e1-82ea-5b25de2490ab","order_by":0,"name":"Ying Yuan","email":"","orcid":"","institution":"Hebei GEO University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Yuan","suffix":""},{"id":507044109,"identity":"58adc122-3821-4f0e-bff4-f45888611d53","order_by":1,"name":"Yunming Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYPACCQYGZuYDBz5USMjJE6+FvS3x4YwzFsaGDURbxHPG2Ji3rSKR4QABhfIzcsykC3Ms8uQjgAzeeRIJjA3MDx/dwKPF4AZQ5cxtEsWGN9LKJOduk8hjZ2AzNs7Bp0UCZPg2icSNM5K3SbwF6mVs4GGTxqcF7DCIlgQzCd45EokNBwhoYbgB1TKf54ixIW8DEVoMzjwrtgZp2QAO5GMSxobNBPwi35688TbvtrrE+c2gqKypk5Nnb374GK/DGDgMINYdgAkw41UOAuwPINY1EFQ5CkbBKBgFIxUAAMWxSwX/d1vUAAAAAElFTkSuQmCC","orcid":"","institution":"Hebei GEO University","correspondingAuthor":true,"prefix":"","firstName":"Yunming","middleName":"","lastName":"Su","suffix":""},{"id":507044110,"identity":"2d231f41-c265-4fd4-b0a6-565a073a5b09","order_by":2,"name":"Mingyu Zhao","email":"","orcid":"","institution":"Hebei GEO University","correspondingAuthor":false,"prefix":"","firstName":"Mingyu","middleName":"","lastName":"Zhao","suffix":""},{"id":507044111,"identity":"ba528f09-ac2c-45d7-ac58-3b612e024188","order_by":3,"name":"Aihong Zhou","email":"","orcid":"","institution":"Hebei GEO University","correspondingAuthor":false,"prefix":"","firstName":"Aihong","middleName":"","lastName":"Zhou","suffix":""},{"id":507044112,"identity":"0e2401c4-cfcc-4ff9-b881-64292fc81cbb","order_by":4,"name":"Lei Ren","email":"","orcid":"","institution":"Hebei Technology Innovation Center for Urban Geological Safety Risk Early Warning","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2025-07-22 15:38:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7188692/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7188692/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10518-026-02375-2","type":"published","date":"2026-02-04T15:57:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102233974,"identity":"2fbc1fd7-12d5-40ad-8a44-25345007eccc","added_by":"auto","created_at":"2026-02-09 16:01:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1386634,"visible":true,"origin":"","legend":"","description":"","filename":"2025725.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7188692/v1_covered_3ef0e5c7-93bc-40fc-90c9-daadc645b3fe.pdf"}],"financialInterests":"","formattedTitle":"A Hybrid SSA-CNN-SVM Model for Seismic-Induced Sand Liquefaction Discrimination","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bulletin-of-earthquake-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"beee","sideBox":"Learn more about [Bulletin of Earthquake Engineering](https://www.springer.com/journal/10518)","snPcode":"10518","submissionUrl":"https://submission.nature.com/new-submission/10518/3","title":"Bulletin of Earthquake Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"‌sand liquefaction, discrimination model, convolutional neural network, support vector machine, sparrow search algorithm","lastPublishedDoi":"10.21203/rs.3.rs-7188692/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7188692/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSeismic-induced sand liquefaction\u0026zwnj; represents \u0026zwnj;a high-impact geohazard\u0026zwnj;, \u0026zwnj;rendering\u0026zwnj; the discrimination\u0026zwnj; and \u0026zwnj;prediction\u0026zwnj; of \u0026zwnj;sand liquefaction states\u0026zwnj; \u0026zwnj;essential\u0026zwnj; for \u0026zwnj;geohazard mitigation research\u0026zwnj;. For the rational discrimination of sand liquefaction states\u0026zwnj;, this study proposes an SSA-CNN-SVM model that integrates Sparrow Search Algorithm (SSA)-optimized Convolutional Neural Networks (CNN) with Support Vector Machines (SVM) for liquefaction discrimination. This model initiates from raw sand liquefaction data, accomplishes layer-by-layer learning to extract liquefaction features and discriminate the states of liquefaction, and employs SVM in lieu of Softmax functions for liquefaction state classification. Taking the sand liquefaction case from the Tangshan earthquake as the comprehensive dataset, the evaluation metrics - standard penetration test (SPT) blow count, mean particle size, coefficient of uniformity, groundwater table depth, effective overburden pressure, seismic intensity, and cyclic shear stress ratio - are input into the SSA-CNN-SVM model for prediction. The results are compared with those from SSA-SVM, SVM, CNN, and Backpropagation Neural Network (BPNN) models, validated against actual sand liquefaction data. The results indicate that the SSA-CNN-SVM model demonstrates superior performance in sand liquefaction discrimination, achieving an accuracy of 83.33%, precision of 83.33%, recall of 83.33%, and F1-Score of 83.33% \u0026ndash; all exceeding corresponding metrics of other comparative models. This validates the high precision of the proposed liquefaction discrimination model and provides a novel approach for practical applications.\u003c/p\u003e","manuscriptTitle":"A Hybrid SSA-CNN-SVM Model for Seismic-Induced Sand Liquefaction Discrimination","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 17:29:34","doi":"10.21203/rs.3.rs-7188692/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-08-28T18:21:02+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-28T16:37:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Bulletin of Earthquake Engineering","date":"2025-08-01T17:01:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T11:36:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bulletin of Earthquake Engineering","date":"2025-07-25T08:42:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bulletin-of-earthquake-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"beee","sideBox":"Learn more about [Bulletin of Earthquake Engineering](https://www.springer.com/journal/10518)","snPcode":"10518","submissionUrl":"https://submission.nature.com/new-submission/10518/3","title":"Bulletin of Earthquake Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"05a07d94-c7bc-4921-99f6-a50d39da38a2","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:00:22+00:00","versionOfRecord":{"articleIdentity":"rs-7188692","link":"https://doi.org/10.1007/s10518-026-02375-2","journal":{"identity":"bulletin-of-earthquake-engineering","isVorOnly":false,"title":"Bulletin of Earthquake Engineering"},"publishedOn":"2026-02-04 15:57:01","publishedOnDateReadable":"February 4th, 2026"},"versionCreatedAt":"2025-09-04 17:29:34","video":"","vorDoi":"10.1007/s10518-026-02375-2","vorDoiUrl":"https://doi.org/10.1007/s10518-026-02375-2","workflowStages":[]},"version":"v1","identity":"rs-7188692","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7188692","identity":"rs-7188692","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}