A Multi-Objective Optimization Approach for Tunnel Construction Methods Incorporating Uncertainty Quantification and Probabilistic Risk Assessment: A Case Study of the Chongqing Central Park Metro Station

A Multi-Objective Optimization Approach for Tunnel Construction Methods Incorporating Uncertainty Quantification and Probabilistic Risk Assessment: A Case Study of the Chongqing Central Park Metro Station | 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 Multi-Objective Optimization Approach for Tunnel Construction Methods Incorporating Uncertainty Quantification and Probabilistic Risk Assessment: A Case Study of the Chongqing Central Park Metro Station Xuanyi Chen, Chao Kong, Keyi Wang, Shengtao Wang, Junru Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8530621/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 Traditional methods for selecting tunnel construction techniques often overlook the uncertainty of surrounding rock parameters and the probability of support failure, leading to limitations in decision-making under complex geological conditions. This paper presents a multi-objective optimization approach for comparing tunnel construction methods, utilizing statistical moment analysis, dimensionality reduction techniques, and conditional failure probability analysis. Using the results of the stratigraphic structural numerical model, the impact of surrounding rock parameter uncertainty on support structure deformation is quantified through the statistical moment method. Additionally, by combining stress-deformation linear fitting, a dynamic evolution model for the probability of support failure is developed. Using Chongqing Rail Transit Line 10's Central Park East Station as a case study, this analysis compares three tunnel construction methods, the double-wall pilot tunnel method, the nine-section segmental temporary support method, and the nine-segment rock column method-based on multi-objective optimization criteria, including construction period, cost, and reliability. The results indicate that the nine-segment rock column method demonstrates optimal overall benefits by ensuring both the stability of the surrounding rock (with a maximum arch deformation of 14.3 mm) and the reliability of the support (with a failure probability of less than 0.8%), while simultaneously reducing the construction period by 20.8% and lowering costs by 25% to 30%. On-site monitoring confirmed the effectiveness of the rock pillar self-supporting system in stress redistribution and deformation control. The study integrates uncertainty analysis and probabilistic risk assessment into tunnel construction method decision-making, providing a scientific foundation for selecting construction methods in tunnel engineering. It demonstrates theoretical innovation and offers broad engineering applicability. Tunnel construction method selection Multi-objective optimization Uncertainty analysis Reliability analysis 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. 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-8530621","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577227525,"identity":"959dbc93-dc74-4305-84d7-24463c452b82","order_by":0,"name":"Xuanyi Chen","email":"","orcid":"","institution":"Southwest University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xuanyi","middleName":"","lastName":"Chen","suffix":""},{"id":577227528,"identity":"e2b2d92a-d0fd-4c86-a1d5-169b7a2525f6","order_by":1,"name":"Chao 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Station","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":"Tunnel construction method selection, Multi-objective optimization, Uncertainty analysis, Reliability analysis","lastPublishedDoi":"10.21203/rs.3.rs-8530621/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8530621/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraditional methods for selecting tunnel construction techniques often overlook the uncertainty of surrounding rock parameters and the probability of support failure, leading to limitations in decision-making under complex geological conditions. This paper presents a multi-objective optimization approach for comparing tunnel construction methods, utilizing statistical moment analysis, dimensionality reduction techniques, and conditional failure probability analysis. Using the results of the stratigraphic structural numerical model, the impact of surrounding rock parameter uncertainty on support structure deformation is quantified through the statistical moment method. Additionally, by combining stress-deformation linear fitting, a dynamic evolution model for the probability of support failure is developed. Using Chongqing Rail Transit Line 10's Central Park East Station as a case study, this analysis compares three tunnel construction methods, the double-wall pilot tunnel method, the nine-section segmental temporary support method, and the nine-segment rock column method-based on multi-objective optimization criteria, including construction period, cost, and reliability. The results indicate that the nine-segment rock column method demonstrates optimal overall benefits by ensuring both the stability of the surrounding rock (with a maximum arch deformation of 14.3 mm) and the reliability of the support (with a failure probability of less than 0.8%), while simultaneously reducing the construction period by 20.8% and lowering costs by 25% to 30%. On-site monitoring confirmed the effectiveness of the rock pillar self-supporting system in stress redistribution and deformation control. The study integrates uncertainty analysis and probabilistic risk assessment into tunnel construction method decision-making, providing a scientific foundation for selecting construction methods in tunnel engineering. It demonstrates theoretical innovation and offers broad engineering applicability.\u003c/p\u003e","manuscriptTitle":"A Multi-Objective Optimization Approach for Tunnel Construction Methods Incorporating Uncertainty Quantification and Probabilistic Risk Assessment: A Case Study of the Chongqing Central Park Metro Station","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 06:58:32","doi":"10.21203/rs.3.rs-8530621/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":"f9af09b3-279b-448f-aa7f-bf2504631b69","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-08T07:38:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 06:58:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8530621","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8530621","identity":"rs-8530621","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}