Intelligent siltation diagnosis model incorporating improved Kolmogorov-Arnold network with transformer network for drainage pipelines in geo-infrastructure

Intelligent siltation diagnosis model incorporating improved Kolmogorov-Arnold network with transformer network for drainage pipelines in geo-infrastructure | 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 Intelligent siltation diagnosis model incorporating improved Kolmogorov-Arnold network with transformer network for drainage pipelines in geo-infrastructure Danyang Di, Baoyong Liao, Shenglin Qin, Hongyuan Fang, Niannian Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8297708/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Diagnosis of underground drainage pipeline siltation is an important means of maintaining and operating buried pipeline geographical infrastructure and preventing and reducing disasters. Deep learning prediction algorithms are widely adopted in intelligent prediction and diagnosis examples related to drainage pipeline operation and maintenance. However, existing prediction neural network algorithms have problems such as being unsuitable for multivariate data sequence analysis, extracting single features, and output results that are prone to partial distortion. This study proposes an intelligent diagnosis model based on the fusion of an improved Kolmogorov-Arnold network (IKAN) module and a Transformer network to address the problem of insufficient extraction of nonlinear features in the diagnosis of siltation in urban drainage pipelines. The model first adopts a IKAN layer constructed using an adaptive grid division and spline interpolation mechanism to convert the nonlinear features of the flow rate and flow velocity data at the inlet and outlet of the pipeline, thereby overcoming the limitations of traditional fixed activation functions that rely too heavily on linear weight matrices and enhancing the model’s ability to express complex data patterns. Subsequently, one-dimensional convolution and fully connected layers are adopted to extract local features, and the Tokenization module is adopted to map the data to a high-dimensional token space. Finally, the multi-layer, multi-head self-attention Transformer network is input to achieve deep modeling of spatiotemporal dependencies, and dual-task learning is adopted to perform regression and classification simultaneously. Compared with other prediction models, the model proposed in this paper is significantly superior to other algorithms in terms of the diagnostic accuracy of the thickness/length of siltation in the pipeline. The experimental results show that the model obtains the best performance when the time step is set to 15 s. The intelligent diagnostic model proposed in this paper has strong robustness and prediction accuracy, achieving 0.282, 0.248 and 0.185 in the evaluation metrics RMSE, MSE and MAPE, respectively, when the proportion of noisy sample data is less than 50%. Physical sciences/Engineering Physical sciences/Mathematics and computing Intelligent diagnosis Underground pipeline siltation Kolmogorov-Arnold network Transformer network Self-diagnosing system Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 31 Dec, 2025 Reviews received at journal 31 Dec, 2025 Reviews received at journal 28 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviewers invited by journal 18 Dec, 2025 Editor assigned by journal 18 Dec, 2025 Editor invited by journal 18 Dec, 2025 Submission checks completed at journal 12 Dec, 2025 First submitted to journal 12 Dec, 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. 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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-8297708","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":562875852,"identity":"88ef586e-ea0e-49c1-91be-ae2125a0da78","order_by":0,"name":"Danyang Di","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Danyang","middleName":"","lastName":"Di","suffix":""},{"id":562875853,"identity":"608ab6fc-26a5-48c6-bd8a-68eb714e2a36","order_by":1,"name":"Baoyong 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diagnosis, Underground pipeline siltation, Kolmogorov-Arnold network, Transformer network, Self-diagnosing system","lastPublishedDoi":"10.21203/rs.3.rs-8297708/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8297708/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiagnosis of underground drainage pipeline siltation is an important means of maintaining and operating buried pipeline geographical infrastructure and preventing and reducing disasters. Deep learning prediction algorithms are widely adopted in intelligent prediction and diagnosis examples related to drainage pipeline operation and maintenance. However, existing prediction neural network algorithms have problems such as being unsuitable for multivariate data sequence analysis, extracting single features, and output results that are prone to partial distortion. This study proposes an intelligent diagnosis model based on the fusion of an improved Kolmogorov-Arnold network (IKAN) module and a Transformer network to address the problem of insufficient extraction of nonlinear features in the diagnosis of siltation in urban drainage pipelines. The model first adopts a IKAN layer constructed using an adaptive grid division and spline interpolation mechanism to convert the nonlinear features of the flow rate and flow velocity data at the inlet and outlet of the pipeline, thereby overcoming the limitations of traditional fixed activation functions that rely too heavily on linear weight matrices and enhancing the model\u0026rsquo;s ability to express complex data patterns. Subsequently, one-dimensional convolution and fully connected layers are adopted to extract local features, and the Tokenization module is adopted to map the data to a high-dimensional token space. Finally, the multi-layer, multi-head self-attention Transformer network is input to achieve deep modeling of spatiotemporal dependencies, and dual-task learning is adopted to perform regression and classification simultaneously. Compared with other prediction models, the model proposed in this paper is significantly superior to other algorithms in terms of the diagnostic accuracy of the thickness/length of siltation in the pipeline. The experimental results show that the model obtains the best performance when the time step is set to 15 s. The intelligent diagnostic model proposed in this paper has strong robustness and prediction accuracy, achieving 0.282, 0.248 and 0.185 in the evaluation metrics RMSE, MSE and MAPE, respectively, when the proportion of noisy sample data is less than 50%.\u003c/p\u003e","manuscriptTitle":"Intelligent siltation diagnosis model incorporating improved Kolmogorov-Arnold network with transformer network for drainage pipelines in geo-infrastructure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 05:54:42","doi":"10.21203/rs.3.rs-8297708/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-31T11:53:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-31T08:44:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-28T12:11:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172807080478279366809061318990041112782","date":"2025-12-19T09:43:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144432188099548677191649068274901962262","date":"2025-12-19T07:52:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-18T12:38:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-18T07:04:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-18T06:59:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-12T15:56:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-12T15:47:11+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":"1ad1e04c-e3eb-42d8-8930-106b4c38653b","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59940339,"name":"Physical sciences/Engineering"},{"id":59940340,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-02-04T07:55:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 05:54:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8297708","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8297708","identity":"rs-8297708","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}