A Modified Triaxial Test Approach for Collapse Potential Assessment: Integrating Experimental Testing with RSM and ANN Predictive Models

A Modified Triaxial Test Approach for Collapse Potential Assessment: Integrating Experimental Testing with RSM and ANN Predictive Models | 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 Modified Triaxial Test Approach for Collapse Potential Assessment: Integrating Experimental Testing with RSM and ANN Predictive Models Seif Eddine KHADRAOUI, Nassima BAKIR, Adam HAMROUNI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8777565/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Collapsible soils in arid regions exhibit metastable structures that undergo severe volume reduction upon wetting, yet systematic evaluation of stress-path effects on collapse magnitude remains limited. This study addresses critical knowledge gaps through comprehensive experimental investigation and advanced predictive modelling of clayey-sand mixture collapse behaviour. Thirty-six laboratory tests systematically examined collapse potential under one-dimensional (K₀) consolidation and isotropic triaxial compression across varying confining pressures (50–200 kPa), moisture contents (2–6%), and relative densities (20–60%). Results demonstrate pronounced stress-path dependency: K₀ consolidation consistently yielded 30.5–56.3% higher collapse magnitudes than triaxial testing under equivalent vertical stresses, attributed to lateral constraint effects and fabric anisotropy. Collapse potential ranged from 1.26% to 14.17%, with confining pressure emerging as the dominant factor (F-value = 299.24), followed by moisture content (78.71) and density (19.95). Post-collapse undrained shearing revealed 35–40% strength degradation, confirming substantial mechanical deterioration. Hybrid computational frameworks combining Response Surface Methodology (R² = 96.11%) and Artificial Neural Networks (validation R = 0.99922) achieved exceptional predictive accuracy while maintaining engineering interpretability. The complementary approach enables rapid collapse estimation without extensive testing, facilitating preliminary design optimization and parametric sensitivity analysis. Findings establish quantitative guidelines for appropriate test selection based on anticipated field stress conditions and provide validated computational tools for collapse prediction in geotechnical practice. The systematic methodology advances understanding of stress-path effects and demonstrates effective integration of statistical and machine learning approaches for complex soil behaviour prediction. Collapsible soil Wetting-induced collapse Stress path dependency Response surface methodology Artificial neural networks Unsaturated soil mechanics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviews received at journal 10 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviewers invited by journal 17 Feb, 2026 Editor assigned by journal 11 Feb, 2026 Submission checks completed at journal 05 Feb, 2026 First submitted to journal 03 Feb, 2026 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-8777565","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592719785,"identity":"55486d1c-b3df-48ce-80bd-2a05f889878a","order_by":0,"name":"Seif Eddine KHADRAOUI","email":"","orcid":"","institution":"University of M’Sila","correspondingAuthor":false,"prefix":"","firstName":"Seif","middleName":"Eddine","lastName":"KHADRAOUI","suffix":""},{"id":592719786,"identity":"9b24e2d9-f29f-42c2-8985-5c07655657d8","order_by":1,"name":"Nassima BAKIR","email":"","orcid":"","institution":"University of M’Sila","correspondingAuthor":false,"prefix":"","firstName":"Nassima","middleName":"","lastName":"BAKIR","suffix":""},{"id":592719787,"identity":"a5b6eb51-9e32-4cfe-8c1a-fce50ad488a5","order_by":2,"name":"Adam HAMROUNI","email":"data:image/png;base64,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","orcid":"","institution":"Mohamed-Cherif Messaadia University","correspondingAuthor":true,"prefix":"","firstName":"Adam","middleName":"","lastName":"HAMROUNI","suffix":""}],"badges":[],"createdAt":"2026-02-03 15:09:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8777565/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8777565/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102971529,"identity":"d86e4962-abca-4b1f-a346-d09416f4f37b","added_by":"auto","created_at":"2026-02-19 06:11:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1873606,"visible":true,"origin":"","legend":"","description":"","filename":"ArticleSeifV1JPR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8777565/v1_covered_ccade8b9-9b9a-454d-ba91-0d94f67cbd38.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Modified Triaxial Test Approach for Collapse Potential Assessment: Integrating Experimental Testing with RSM and ANN Predictive Models","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":"journal-of-building-pathology-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpar","sideBox":"Learn more about [Journal of Building Pathology and Rehabilitation](http://link.springer.com/journal/41024)","snPcode":"41024","submissionUrl":"https://submission.nature.com/new-submission/41024/3","title":"Journal of Building Pathology and Rehabilitation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Collapsible soil, Wetting-induced collapse, Stress path dependency, Response surface methodology, Artificial neural networks, Unsaturated soil mechanics","lastPublishedDoi":"10.21203/rs.3.rs-8777565/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8777565/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCollapsible soils in arid regions exhibit metastable structures that undergo severe volume reduction upon wetting, yet systematic evaluation of stress-path effects on collapse magnitude remains limited. This study addresses critical knowledge gaps through comprehensive experimental investigation and advanced predictive modelling of clayey-sand mixture collapse behaviour. Thirty-six laboratory tests systematically examined collapse potential under one-dimensional (K₀) consolidation and isotropic triaxial compression across varying confining pressures (50\u0026ndash;200 kPa), moisture contents (2\u0026ndash;6%), and relative densities (20\u0026ndash;60%). Results demonstrate pronounced stress-path dependency: K₀ consolidation consistently yielded 30.5\u0026ndash;56.3% higher collapse magnitudes than triaxial testing under equivalent vertical stresses, attributed to lateral constraint effects and fabric anisotropy. Collapse potential ranged from 1.26% to 14.17%, with confining pressure emerging as the dominant factor (F-value\u0026thinsp;=\u0026thinsp;299.24), followed by moisture content (78.71) and density (19.95). Post-collapse undrained shearing revealed 35\u0026ndash;40% strength degradation, confirming substantial mechanical deterioration. Hybrid computational frameworks combining Response Surface Methodology (R\u0026sup2; = 96.11%) and Artificial Neural Networks (validation R\u0026thinsp;=\u0026thinsp;0.99922) achieved exceptional predictive accuracy while maintaining engineering interpretability. The complementary approach enables rapid collapse estimation without extensive testing, facilitating preliminary design optimization and parametric sensitivity analysis. Findings establish quantitative guidelines for appropriate test selection based on anticipated field stress conditions and provide validated computational tools for collapse prediction in geotechnical practice. The systematic methodology advances understanding of stress-path effects and demonstrates effective integration of statistical and machine learning approaches for complex soil behaviour prediction.\u003c/p\u003e","manuscriptTitle":"A Modified Triaxial Test Approach for Collapse Potential Assessment: Integrating Experimental Testing with RSM and ANN Predictive Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 06:10:37","doi":"10.21203/rs.3.rs-8777565/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-02T16:31:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-02T15:39:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332037017613325980315592002404388086692","date":"2026-03-23T15:26:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T10:58:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336633009008711027510500641476207782557","date":"2026-03-10T10:44:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T08:32:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-11T21:17:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-05T18:39:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Building Pathology and Rehabilitation","date":"2026-02-03T14:27:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-building-pathology-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpar","sideBox":"Learn more about [Journal of Building Pathology and Rehabilitation](http://link.springer.com/journal/41024)","snPcode":"41024","submissionUrl":"https://submission.nature.com/new-submission/41024/3","title":"Journal of Building Pathology and Rehabilitation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b7f58fa4-f85c-4222-b484-79001291a897","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T06:24:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 06:10:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8777565","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8777565","identity":"rs-8777565","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}