The Forecasting of Surface Displacement for Tunnel Slopes Utilizing the WD-IPSO-GRU Model

The Forecasting of Surface Displacement for Tunnel Slopes Utilizing the WD-IPSO-GRU Model | 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 The Forecasting of Surface Displacement for Tunnel Slopes Utilizing the WD-IPSO-GRU Model Guoqing MA, Xiaopeng Zang, Shitong Chen, Momo Zhi, Xiaoming Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4246841/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted 16 You are reading this latest preprint version Abstract Continuous displacement prediction of tunnel slope deformation can serve as a basis for evaluating slope stability. For this purpose, a fusion optimized prediction model based on wavelet decomposition (WD), particle swarm optimization with genetic algorithm enhancement (IPSO), and gated recurrent unit (GRU) termed WD-IPSO-GRU is proposed. Initially, WD preprocesses noise and features in field displacement monitoring data; subsequently, IPSO dynamically sets learning factors and weights, optimizing the number of neurons and iteration times in GRU hidden layers L1 and L2, and introduces Dropout technique to prevent overfitting, enhancing GRU model performance in long-term sequence prediction tasks. Finally, leveraging the optimal solution enables prediction of GNSS displacement of tunnel slope surfaces. Results indicate that compared to GRU, recurrent neural network (RNN), and long short-term memory (LSTM) models, the WD-IPSO-GRU model demonstrates higher prediction accuracy. The root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²) for site 02 are 0.16, 0.18%, and 0.95 respectively, providing a new approach for tunnel slope displacement prediction. WD Dropout Technique IPSO GA GRU Analytical Prediction of Slope Displacement Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Jun, 2024 Reviews received at journal 06 Jun, 2024 Reviews received at journal 06 Jun, 2024 Reviews received at journal 05 Jun, 2024 Reviews received at journal 01 Jun, 2024 Reviewers agreed at journal 22 May, 2024 Reviewers agreed at journal 22 May, 2024 Reviewers agreed at journal 22 May, 2024 Reviewers agreed at journal 20 May, 2024 Reviews received at journal 20 May, 2024 Reviewers agreed at journal 19 May, 2024 Reviewers invited by journal 19 May, 2024 Editor assigned by journal 09 May, 2024 Editor invited by journal 23 Apr, 2024 Submission checks completed at journal 23 Apr, 2024 First submitted to journal 10 Apr, 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. 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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-4246841","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":294739381,"identity":"39b5fe85-8f1a-43f7-a223-9a3ae341d4b7","order_by":0,"name":"Guoqing MA","email":"","orcid":"","institution":"Shijiazhuang Tiedao University","correspondingAuthor":false,"prefix":"","firstName":"Guoqing","middleName":"","lastName":"MA","suffix":""},{"id":294739382,"identity":"64409807-0878-4b9a-bc36-f48e17ff710b","order_by":1,"name":"Xiaopeng Zang","email":"","orcid":"","institution":"Shijiazhuang Tiedao University","correspondingAuthor":false,"prefix":"","firstName":"Xiaopeng","middleName":"","lastName":"Zang","suffix":""},{"id":294739383,"identity":"428cdb0f-959e-4c34-8761-337f81cffff5","order_by":2,"name":"Shitong Chen","email":"","orcid":"","institution":"Shijiazhuang Tiedao University","correspondingAuthor":false,"prefix":"","firstName":"Shitong","middleName":"","lastName":"Chen","suffix":""},{"id":294739384,"identity":"308278a4-bf7f-4384-89a9-3566ecf3a82a","order_by":3,"name":"Momo Zhi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDACCcYGKIuH8UFCRQ1pWpgNHpw5RowWOIuHTfJhCzNhHfKzm1s3fMyxkd0ukXusIrGBjYG/vTsBrxbGOQfbbs7clma8c0Ze2o3EHTIMEmfObsCrhVkise0277bDiRtu5JjdSDzDxmAgkYtfCxuyloLENmbCWniQtTAQpUUCqAXslw1n3hhLJJw5xkPQL/Iz0p/d+LjNRnbD8RzDjz8qauT423vxa4EBxgaBBIhLiVIO0cJ/gGjFo2AUjIJRMMIAAPrjT1vlc5dwAAAAAElFTkSuQmCC","orcid":"","institution":"Shijiazhuang Tiedao University","correspondingAuthor":true,"prefix":"","firstName":"Momo","middleName":"","lastName":"Zhi","suffix":""},{"id":294739385,"identity":"1dff6706-802c-419f-8100-17d4c349b043","order_by":4,"name":"Xiaoming Huang","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoming","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-04-10 10:48:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4246841/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4246841/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-71742-3","type":"published","date":"2024-09-05T16:05:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64185952,"identity":"34f9bb07-28ba-408c-96b3-0c9c75552727","added_by":"auto","created_at":"2024-09-09 16:23:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1057212,"visible":true,"origin":"","legend":"","description":"","filename":"TheForecastingofSurfaceDisplacementforTunnelSlopesUtilizingtheWDIPSOGRUModel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4246841/v1_covered_2f6224b8-51a4-48b1-aae8-b741e86ccd38.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Forecasting of Surface Displacement for Tunnel Slopes Utilizing the WD-IPSO-GRU Model","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":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":"WD, Dropout Technique, IPSO, GA, GRU, Analytical Prediction of Slope Displacement","lastPublishedDoi":"10.21203/rs.3.rs-4246841/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4246841/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Continuous displacement prediction of tunnel slope deformation can serve as a basis for evaluating slope stability. For this purpose, a fusion optimized prediction model based on wavelet decomposition (WD), particle swarm optimization with genetic algorithm enhancement (IPSO), and gated recurrent unit (GRU) termed WD-IPSO-GRU is proposed. Initially, WD preprocesses noise and features in field displacement monitoring data; subsequently, IPSO dynamically sets learning factors and weights, optimizing the number of neurons and iteration times in GRU hidden layers L1 and L2, and introduces Dropout technique to prevent overfitting, enhancing GRU model performance in long-term sequence prediction tasks. Finally, leveraging the optimal solution enables prediction of GNSS displacement of tunnel slope surfaces. Results indicate that compared to GRU, recurrent neural network (RNN), and long short-term memory (LSTM) models, the WD-IPSO-GRU model demonstrates higher prediction accuracy. The root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²) for site 02 are 0.16, 0.18%, and 0.95 respectively, providing a new approach for tunnel slope displacement prediction.","manuscriptTitle":"The Forecasting of Surface Displacement for Tunnel Slopes Utilizing the WD-IPSO-GRU Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-26 18:21:31","doi":"10.21203/rs.3.rs-4246841/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-12T08:10:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-06T09:49:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-06T07:22:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-05T07:01:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-01T15:06:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185008461446064877883073034537943895034","date":"2024-05-22T07:02:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55878873184807922722677661601982371343","date":"2024-05-22T06:46:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270420940276503599151626969689866429050","date":"2024-05-22T04:36:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267864605619412148021261631571097890500","date":"2024-05-20T11:18:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-20T06:31:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245002723506613909174956489878818045437","date":"2024-05-20T03:45:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-20T02:47:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-09T06:49:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-23T09:16:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-23T09:14:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-10T10:47:08+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":"4aa7d9d3-cf3a-403a-9159-4d7878e8d8a4","owner":[],"postedDate":"April 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-09T16:13:36+00:00","versionOfRecord":{"articleIdentity":"rs-4246841","link":"https://doi.org/10.1038/s41598-024-71742-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-09-05 16:05:37","publishedOnDateReadable":"September 5th, 2024"},"versionCreatedAt":"2024-04-26 18:21:31","video":"","vorDoi":"10.1038/s41598-024-71742-3","vorDoiUrl":"https://doi.org/10.1038/s41598-024-71742-3","workflowStages":[]},"version":"v1","identity":"rs-4246841","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4246841","identity":"rs-4246841","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}