Dominant Factors and Prediction Model of Toppling Collapse for Steep Cliffs in Strong Earthquake Zones

Dominant Factors and Prediction Model of Toppling Collapse for Steep Cliffs in Strong Earthquake Zones | 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 Dominant Factors and Prediction Model of Toppling Collapse for Steep Cliffs in Strong Earthquake Zones Naman Maimaiti, Ruiming Liu, Peng Zhang, Jili Qu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7646891/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Earthquake-induced collapses of steep cliffs with hazardous rock masses represent a prevalent geological hazard in mountainous regions. Current stability assessment methods for grouped rock failures under seismic loading exhibit significant limitatiion. This study systematically investigates failure modes, critical collapse thresholds, and risk factors through an integrated approach combining scaled shaking table tests and 3DEC discrete element modeling, focusing on hazardous rock formations along the China-Pakistan Highway.ons, particularly in addressing complex failure mechanisms like toppling collapse. Key findings reveal: (1) The displacement angle (≈ 15°) serves as a robust collapse indicator, outperforming conventional metrics; (2) Among four primary influencing factors (rock fragmentation, joint inclination, height, and strength), weathering degree emerges as the dominant control; (3) Our developed BP neural network model identifies toppling and sliding as predominant failure modes, with joint inclination being the most critical controlling parameter; (4) Collapse initiation demonstrates nonlinear dependence on rock geometry (size, aspect ratio, and layering configuration), where increased joint inclination accelerates failure while reduced rock size promotes rapid collapse progression. The derived critical vibration velocity establishes a reliable criterion for toppling collapse prediction. Factor importance analysis ranks controlling parameters as: rock size > joint inclination > shape > layering configuration. These findings provide a theoretical framework for predicting and preventing seismic-induced rock collapses in earthquake-prone regions. Physical sciences/Engineering Earth and environmental sciences/Natural hazards Earth and environmental sciences/Solid earth sciences steep cliff hazardous rocks model test 3DEC numerical simulation instability mode critical collapse vibration speed BP neural network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 29 Sep, 2025 Reviewers invited by journal 23 Sep, 2025 Editor invited by journal 23 Sep, 2025 Editor assigned by journal 23 Sep, 2025 Submission checks completed at journal 22 Sep, 2025 First submitted to journal 18 Sep, 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. 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-7646891","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":524485497,"identity":"d0e950df-26da-4e0d-a2cc-3153a20b3766","order_by":0,"name":"Naman Maimaiti","email":"","orcid":"","institution":"Nanjing Tech University","correspondingAuthor":false,"prefix":"","firstName":"Naman","middleName":"","lastName":"Maimaiti","suffix":""},{"id":524485498,"identity":"f2389d06-809d-4e22-aee7-ac8fb371125a","order_by":1,"name":"Ruiming Liu","email":"","orcid":"","institution":"Kashi 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Zones","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"steep cliff hazardous rocks, model test, 3DEC numerical simulation, instability mode, critical collapse vibration speed, BP neural network","lastPublishedDoi":"10.21203/rs.3.rs-7646891/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7646891/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEarthquake-induced collapses of steep cliffs with hazardous rock masses represent a prevalent geological hazard in mountainous regions. Current stability assessment methods for grouped rock failures under seismic loading exhibit significant limitatiion. This study systematically investigates failure modes, critical collapse thresholds, and risk factors through an integrated approach combining scaled shaking table tests and 3DEC discrete element modeling, focusing on hazardous rock formations along the China-Pakistan Highway.ons, particularly in addressing complex failure mechanisms like toppling collapse. Key findings reveal: (1) The displacement angle (\u0026asymp;\u0026thinsp;15\u0026deg;) serves as a robust collapse indicator, outperforming conventional metrics; (2) Among four primary influencing factors (rock fragmentation, joint inclination, height, and strength), weathering degree emerges as the dominant control; (3) Our developed BP neural network model identifies toppling and sliding as predominant failure modes, with joint inclination being the most critical controlling parameter; (4) Collapse initiation demonstrates nonlinear dependence on rock geometry (size, aspect ratio, and layering configuration), where increased joint inclination accelerates failure while reduced rock size promotes rapid collapse progression. The derived critical vibration velocity establishes a reliable criterion for toppling collapse prediction. Factor importance analysis ranks controlling parameters as: rock size\u0026thinsp;\u0026gt;\u0026thinsp;joint inclination\u0026thinsp;\u0026gt;\u0026thinsp;shape\u0026thinsp;\u0026gt;\u0026thinsp;layering configuration. These findings provide a theoretical framework for predicting and preventing seismic-induced rock collapses in earthquake-prone regions.\u003c/p\u003e","manuscriptTitle":"Dominant Factors and Prediction Model of Toppling Collapse for Steep Cliffs in Strong Earthquake Zones","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 10:40:32","doi":"10.21203/rs.3.rs-7646891/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"228830708438385617760168381529428051739","date":"2025-09-29T06:20:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-24T01:46:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-23T19:08:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-23T19:00:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-22T11:03:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-18T07:55:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b81a8c58-2ae9-4816-8665-a1132933a8ab","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55747820,"name":"Physical sciences/Engineering"},{"id":55747821,"name":"Earth and environmental sciences/Natural hazards"},{"id":55747822,"name":"Earth and environmental sciences/Solid earth sciences"}],"tags":[],"updatedAt":"2026-01-20T15:17:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 10:40:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7646891","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7646891","identity":"rs-7646891","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}