Integrated Text - Model Generation – Simulation – Machine Learning Framework for Developing a Temperature Field Model of 3-Side Protection Steel Beam

Integrated Text - Model Generation – Simulation – Machine Learning Framework for Developing a Temperature Field Model of 3-Side Protection Steel Beam | 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 Integrated Text - Model Generation – Simulation – Machine Learning Framework for Developing a Temperature Field Model of 3-Side Protection Steel Beam Yang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4293443/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Automating the modelling and simulation process benefits all related areas, such as research, design, manufacturing and especially the training of machine learning models. This approachsignificantly improves the modelling efficiency and speed, providing scalability in data generation, which motivates the discovery of new patterns. In addition, consistency and reproducibility through unified modellingmethods improve accuracy by eliminating manual mistakes in repetitive processes. Machine learning provides an efficient approach foraccessing large-scale simulation data and providing insights into unknown circumstances. Three-sided protection steel beams arecommon yet rarely investigated section types in offshore oil platforms. With the top side of the upper flange exposed to fire, large convective and radiative heat fluxes are induced, leading to a rapidly descending temperature diagram along the web. The increase in thelever arm caused by the decrease in the elastic neutral axis under this temperature distribution and the decrease in the elasticity of the compressed flange cause earlier lateral torsional buckling failure compared to that of the 4-sided protection beam. To provide a close temperature profile, an integrated framework automating modelling, simulation, data processing and machining learning sequence using the ABAQUS kernel scripting method, Python and MATLAB is proposed. The modelling method automates the model generation process with inputs from a parameter text file and establishes restraints using the edge contact-detection algorithm for unusual shapes. Second, the model files (.inp) are submitted to ABAQUS, and MATLAB controls the simulation process. The output data are extracted and written into .csv files. Third, the extracted data are divided by Python code into data batches and fed to machine learning models for training. All IPE, universal beam sections and 54 weldedsections from a realistic oil processing structure with different protection limits are tested with the protection thicknesses calibrated with an optimisation program with a 5 °C allowance. The gradient boosting method achieves a root mean square error of 1.34 °C compared to the simulation results. The calculation time of the developed software with a graphical user interface is also tested with various numbersof temperature points and output intervals. Civil Engineering Automatic modelling Machine learning ABAQUS kernel scripting method Fire protection Steel beam Full Text Additional Declarations The authors declare no competing interests. Supplementary Files Edgedetection.mp4 Edge_detection_algorithm Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4293443","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299251679,"identity":"9603909a-727c-4e01-86d6-9fcf266cc9e4","order_by":0,"name":"Yang Li","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-2988-3946","institution":"The University of Manchester","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-04-19 13:31:31","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4293443/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-4293443/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56489461,"identity":"3a566582-e590-41a5-8b4a-1f9d9f27669e","added_by":"auto","created_at":"2024-05-14 22:00:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3493837,"visible":true,"origin":"","legend":"","description":"","filename":"Integratedframework.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4293443/v2_covered_dbdec262-41f5-4a69-bc53-fbf02e08f2cf.pdf"},{"id":56489229,"identity":"3c39d90f-74c9-4339-8733-df6d329beb2b","added_by":"auto","created_at":"2024-05-14 21:44:08","extension":"mp4","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2137784,"visible":true,"origin":"","legend":"\u003cp\u003eEdge_detection_algorithm\u003c/p\u003e","description":"","filename":"Edgedetection.mp4","url":"https://assets-eu.researchsquare.com/files/rs-4293443/v2/6e40afc733bddbfca019ac15.mp4"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Integrated Text - Model Generation – Simulation – Machine Learning Framework for Developing a Temperature Field Model of 3-Side Protection Steel Beam","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Manchester","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Automatic modelling, Machine learning, ABAQUS kernel scripting method, Fire protection, Steel beam","lastPublishedDoi":"10.21203/rs.3.rs-4293443/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4293443/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAutomating the modelling and simulation process benefits all related areas, such as research, design, manufacturing and especially the training of machine learning models. This approachsignificantly improves the modelling efficiency and speed, providing scalability in data generation, which motivates the discovery of new patterns. In addition, consistency and reproducibility through unified modellingmethods improve accuracy by eliminating manual mistakes in repetitive processes. Machine learning provides an efficient approach foraccessing large-scale simulation data and providing insights into unknown circumstances. Three-sided protection steel beams arecommon yet rarely investigated section types in offshore oil platforms. With the top side of the upper flange exposed to fire, large convective and radiative heat fluxes are induced, leading to a rapidly descending temperature diagram along the web. The increase in thelever arm caused by the decrease in the elastic neutral axis under this temperature distribution and the decrease in the elasticity of the compressed flange cause earlier lateral torsional buckling failure compared to that of the 4-sided protection beam. To provide a close temperature profile, an integrated framework automating modelling, simulation, data processing and machining learning sequence using the ABAQUS kernel scripting method, Python and MATLAB is proposed. The modelling method automates the model generation process with inputs from a parameter text file and establishes restraints using the edge contact-detection algorithm for unusual shapes. Second, the model files (.inp) are submitted to ABAQUS, and MATLAB controls the simulation process. The output data are extracted and written into .csv files. Third, the extracted data are divided by Python code into data batches and fed to machine learning models for training. All IPE, universal beam sections and 54 weldedsections from a realistic oil processing structure with different protection limits are tested with the protection thicknesses calibrated with an optimisation program with a 5 °C allowance. The gradient boosting method achieves a root mean square error of 1.34 °C compared to the simulation results. The calculation time of the developed software with a graphical user interface is also tested with various numbersof temperature points and output intervals.\u003c/p\u003e","manuscriptTitle":"Integrated Text - Model Generation – Simulation – Machine Learning Framework for Developing a Temperature Field Model of 3-Side Protection Steel Beam","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2024-05-14 21:44:01","doi":"10.21203/rs.3.rs-4293443/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2024-04-22 02:42:40","doi":"10.21203/rs.3.rs-4293443/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"94da543e-16cc-4679-b4a7-4e2a2d9dc8f9","owner":[],"postedDate":"May 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31569057,"name":"Civil Engineering"}],"tags":[],"updatedAt":"2024-04-22T02:42:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-14 21:44:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-4293443","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4293443","identity":"rs-4293443","version":["v2"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}