{"paper_id":"d5ea0163-e24a-4a31-99c3-d64625334126","body_text":"Corrosion Potential Prediction of Marine Engineering Steel Based on Machine Learning | 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 Corrosion Potential Prediction of Marine Engineering Steel Based on Machine Learning Bin Wu, Yicong Luo, Shiwei Yu, Endian Fan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8675886/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 Corrosion is a major cause of failure in marine engineering steels, resulting in large economic losses worldwide. This study combines marine corrosion knowledge with machine learning techniques to predict corrosion potential. Using experimental data collected from many published studies, five machine learning models were built in Python: K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Gradient Boosting Regressor (GBR), Stacked Generalization (Stacking), and a Weighted Average Ensemble. Model prediction accuracy was improved through feature engineering and data augmentation. The XGBoost model performed best and achieved a coefficient of determination (R²) of 0.80 on the training set and 0.62 on the test set. Its mean absolute error (MAE) was 0.07 V and root mean square error (RMSE) was 0.09 V. The generalization gap was 0.179. Feature importance analysis revealed that Mn, Cr, and the Cr×Mo interaction are key factors influencing corrosion potential. This approach provides a accurate and interpretable technical solution to predict corrosion potential for marine engineering steels. This study offers valuable insights for optimizing steel composition and enhancing corrosion-resistant design. Physical sciences/Engineering Physical sciences/Materials science Marine engineering steel Corrosion potential prediction Machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Mar, 2026 Reviews received at journal 28 Feb, 2026 Reviews received at journal 27 Feb, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviews received at journal 05 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers invited by journal 04 Feb, 2026 Editor assigned by journal 28 Jan, 2026 Submission checks completed at journal 28 Jan, 2026 First submitted to journal 23 Jan, 2026 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-8675886\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":586103655,\"identity\":\"a432bd7c-5857-40c7-aa71-e033babf96cd\",\"order_by\":0,\"name\":\"Bin Wu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Jiangsu Ocean University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Bin\",\"middleName\":\"\",\"lastName\":\"Wu\",\"suffix\":\"\"},{\"id\":586103658,\"identity\":\"503b1f1d-fc60-48d0-b2e0-51c6f2409763\",\"order_by\":1,\"name\":\"Yicong Luo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Jiangsu Ocean University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yicong\",\"middleName\":\"\",\"lastName\":\"Luo\",\"suffix\":\"\"},{\"id\":586103660,\"identity\":\"2e4b6db4-6888-4004-a36b-31ed70b7bdf4\",\"order_by\":2,\"name\":\"Shiwei Yu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Jiangsu Ocean University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shiwei\",\"middleName\":\"\",\"lastName\":\"Yu\",\"suffix\":\"\"},{\"id\":586103662,\"identity\":\"8296bd24-c743-4150-afbd-57046984264b\",\"order_by\":3,\"name\":\"Endian Fan\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBAC9gYQySYBZDU2PvxAjBaeAzAtPIebjSVI0ALEEultAjxEaWE/e/BxQZmFvLnkwzYGCQY7Od0GQlp48pKNZ5yTMNw5O7HtQQFDsrHZAQJa7BlyzKR52yQYN9xObDeQYDiQuI2QFh7+N+a/gVrsN9w82CbBQ5QWiRwzZqCWxA03GInW8sZYmuecRPKGM4nAQDYgwi88/DmGn3nK6mw3HD/+8OGHCjs5glrQgAFpykfBKBgFo2AU4AAAm8Y9qHKBLNAAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Jiangsu Ocean University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Endian\",\"middleName\":\"\",\"lastName\":\"Fan\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-01-23 06:53:43\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8675886/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8675886/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":102295878,\"identity\":\"d2779043-1ecb-4b5a-b82c-3b125ea2e592\",\"added_by\":\"auto\",\"created_at\":\"2026-02-10 10:15:46\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1330615,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8675886/v1_covered_99971427-55e8-4728-94b9-43aba42a6e44.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Corrosion Potential Prediction of Marine Engineering Steel Based on Machine Learning\",\"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\":\"info@researchsquare.com\",\"identity\":\"npj-materials-degradation\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"npjmatdeg\",\"sideBox\":\"Learn more about [npj Materials Degradation](http://www.nature.com/npjmatdeg/)\",\"snPcode\":\"41529\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/41529/3\",\"title\":\"npj Materials Degradation\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"NPJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Marine engineering steel, Corrosion potential prediction, Machine learning\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8675886/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8675886/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eCorrosion is a major cause of failure in marine engineering steels, resulting in large economic losses worldwide. This study combines marine corrosion knowledge with machine learning techniques to predict corrosion potential. Using experimental data collected from many published studies, five machine learning models were built in Python: K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Gradient Boosting Regressor (GBR), Stacked Generalization (Stacking), and a Weighted Average Ensemble. Model prediction accuracy was improved through feature engineering and data augmentation. The XGBoost model performed best and achieved a coefficient of determination (R\\u0026sup2;) of 0.80 on the training set and 0.62 on the test set. Its mean absolute error (MAE) was 0.07 V and root mean square error (RMSE) was 0.09 V. The generalization gap was 0.179. Feature importance analysis revealed that Mn, Cr, and the Cr\\u0026times;Mo interaction are key factors influencing corrosion potential. 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