Machine learning prediction and parameter sensitivity analysis of top earth pressure of high-filled cut-and-cover tunnels

Machine learning prediction and parameter sensitivity analysis of top earth pressure of high-filled cut-and-cover tunnels | 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 Machine learning prediction and parameter sensitivity analysis of top earth pressure of high-filled cut-and-cover tunnels Sheng Li, Zhenhui Lang, Ke Zhang, Qingyu Meng, Tianwei Feng, Xiaoning Cui, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6313043/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract To quickly and effectively predict the unknown top earth pressure of high-filled cut-and-cover tunnels (HFCCTs), a prediction model of top earth pressure of HFCCTs based on machine learning was proposed. The data set was established by taking Poisson 's ratio, friction angle, cohesion, the ratio of groove width to HFCCT width, slope angle and filling height as the input parameters of ML models, and taking the maximum vertical top earth pressure of HFCCTs as the output parameter, and the correlation between the input parameters was analyzed. The Newton-Raphson-based optimization (NRBO) was used to optimize the hyper-parameters of the XGBoost model, and compared with the XGBoost, SVM, RF, BP models under grid search. The SHAP method was used to analyze the sensitivity of the input parameters of the NRBO-XGBoost model. Finally, based on the field measured data of an airport high-speed railway tunnel, the engineering applicability of the proposed earth pressure prediction model was verified. The results revealed that among the input parameters, the filling height was the most influential factor. The prediction performance of NRBO-XGBoost model was better than that of other traditional ML models, and it has good engineering applicability, which can provide an effective method and basis for judging the stability state of HFCCTs in practical geotechnical engineering. Top earth pressure High-filled cut-and-cover tunnel Machine learning Sensitivity analysis Engineering applicability Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 May, 2025 Reviews received at journal 03 May, 2025 Reviews received at journal 04 Apr, 2025 Reviewers agreed at journal 30 Mar, 2025 Reviewers agreed at journal 29 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers invited by journal 28 Mar, 2025 Editor assigned by journal 27 Mar, 2025 Submission checks completed at journal 27 Mar, 2025 First submitted to journal 26 Mar, 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-6313043","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439618532,"identity":"c791fd97-69e8-4097-85fa-483976e35ed5","order_by":0,"name":"Sheng 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