Mixed Monitoring Model of Concrete Arch Dam on Residual Sequence Correction

Mixed Monitoring Model of Concrete Arch Dam on Residual Sequence Correction | 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 Mixed Monitoring Model of Concrete Arch Dam on Residual Sequence Correction Zixuan Wang, Shuyan Fu, Dehui Chen, Haoquan Chi, Ou Bin, Meng Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4699602/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Under the dual influence of internal factors and the external environment, dam deformation shows a high degree of nonlinear characteristics. In the traditional dam deformation prediction model, an abnormal value of the residual sequence reflects an abnormal situation or potential fault in the dam deformation prediction. Therefore, it is particularly important to fully mine the effective information in the residual sequence. Considering the nonlinear, time-varying and chaotic characteristics of dam time series, a hybrid monitoring model of concrete arch dams based on residual sequence correction is proposed in this paper. Firstly, combined with the finite element method, the statistical model was used to establish the initial mixed prediction model based on the monitoring data. In view of the specific characteristics and periodicity of the prediction residuals of the hybrid model, this paper uses symplectic geometric mode decomposition (SGMD) to decompose the residual sequence. Then, the convolutional neural network (CNN) is optimized by the improved sparrow search algorithm (ISSA), and the decomposed modal components are predicted and reconstructed by the bidirectional gated recurrent unit (BiGRU) of the attention mechanism. Subsequently, the differential autoregressive moving average model (ARIMA) is used to correct the reconstructed residual sequence. Finally, the modified residual sequence is combined with the initial hybrid model to construct a hybrid monitoring model of a concrete arch dam based on residual sequence correction. Through the refinement of the residuals, the accuracy and reliability of the monitoring data are further improved. This provides a new and effective method for safety monitoring and early warning of concrete arch dams. concrete arch dams hybrid monitoring model improved sparrow search algorithm convolutional neural networks bidirectional gated recurrent units autoregressive integrated moving average model Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 27 Aug, 2024 Reviewers agreed at journal 18 Jul, 2024 Reviewers invited by journal 17 Jul, 2024 Editor assigned by journal 08 Jul, 2024 First submitted to journal 07 Jul, 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. 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-4699602","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328169118,"identity":"8f2e768e-a85a-4e63-b740-5905ad55bf3b","order_by":0,"name":"Zixuan Wang","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zixuan","middleName":"","lastName":"Wang","suffix":""},{"id":328169119,"identity":"5573e085-b79c-429a-b681-1824cbe91fed","order_by":1,"name":"Shuyan Fu","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shuyan","middleName":"","lastName":"Fu","suffix":""},{"id":328169120,"identity":"9a3e6cf1-c82e-4cc6-9111-061c0e965a7e","order_by":2,"name":"Dehui Chen","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Dehui","middleName":"","lastName":"Chen","suffix":""},{"id":328169121,"identity":"501d1edb-4243-4017-bd0a-4c804855c417","order_by":3,"name":"Haoquan Chi","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Haoquan","middleName":"","lastName":"Chi","suffix":""},{"id":328169122,"identity":"266544b7-d794-4377-ba8e-6b0e19bc16bf","order_by":4,"name":"Ou Bin","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Ou","middleName":"","lastName":"Bin","suffix":""},{"id":328169123,"identity":"b8b50455-6a41-41e2-8216-d5ddcf047ad1","order_by":5,"name":"Meng Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYNACg39y/MzMBx+QoKXigLFkO1uyAQlazhxI3HCex0yAOCddO2O6gbHtTuLmwwxmDAw1NtGEtdzOMbvB2PbMeNthhrQHDMfSchuI1MIsC9Ry3ICx4TDxWhg3NzO2SRCvheHMYcUNzMxsxGmRvJ1WdoOhIs1Y4jAbs0ECMX7hu5287QaDgY0cf//5jw8+1NgQ1qJwgMOA+Q+Ml0BIOQjIN7A/IEbdKBgFo2AUjGQAANN3RDMzkBKMAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing Hydraulic Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Meng","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-07-07 09:35:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4699602/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4699602/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62253073,"identity":"8dcaa39d-4437-41f0-bada-faf913cfea62","added_by":"auto","created_at":"2024-08-12 06:42:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1609671,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4699602/v1_covered_e4d24194-6b0c-4b0f-8e1d-fafb157102eb.pdf"}],"financialInterests":"","formattedTitle":"Mixed Monitoring Model of Concrete Arch Dam on Residual Sequence Correction","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":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"concrete arch dams, hybrid monitoring model, improved sparrow search algorithm, convolutional neural networks, bidirectional gated recurrent units, autoregressive integrated moving average model","lastPublishedDoi":"10.21203/rs.3.rs-4699602/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4699602/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Under the dual influence of internal factors and the external environment, dam deformation shows a high degree of nonlinear characteristics. 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