Study on the Mechanical Properties Equivalence of High-Temperature Red Sandstone and Neural Network Prediction

Study on the Mechanical Properties Equivalence of High-Temperature Red Sandstone and Neural Network Prediction | 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 Study on the Mechanical Properties Equivalence of High-Temperature Red Sandstone and Neural Network Prediction Yifan Zhang, Mingze Qin, Nan Qin, Tianxiang Sun, Dongxu Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5300674/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Post-disaster assessment is an important problem in engineering field, and cooling methods after high temperature are important factors to be considered. Purpose In order to explore the damage characteristics of red sandstone after heat impact, and realize the damage assessment and quantization. Methods Red sandstone specimens were heated respectively at temperature ranging from 200℃ to 700℃, and were cooled by air or water. In addition, a improved Nishihara model was used to construct a constitutive model of heat impact damage and validated using a neural network model. Results The test results indicate that: the peak strength of the red sandstone is bounded by 400℃, which is first increased and then decreases. And the strength of the water-cooled samples are less than that of the air-cooled samples. Both the improved Nishihara model and neural network model have high correlation coefficients and can achieve the damage assessment under different temperature and cooling rates. Conclusions There is a temperature threshold, so that the peak strength first increases and then decreases. The cooling rate will enhance the heat impact damage and aggravate the deterioration of the physical and mechanical properties. The improved Nishihara model and neural network model can achieve damage prediction. High temperature cooling method heat impact damage red sandstone Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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-5300674","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372143414,"identity":"7d64117c-5b6a-4037-a338-b22068da33ea","order_by":0,"name":"Yifan Zhang","email":"","orcid":"","institution":"Qingdao University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Zhang","suffix":""},{"id":372143415,"identity":"d428eb90-7159-4158-9570-8ec8949a9c4f","order_by":1,"name":"Mingze Qin","email":"","orcid":"","institution":"Qingdao University of Science and 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sandstone","lastPublishedDoi":"10.21203/rs.3.rs-5300674/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5300674/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePost-disaster assessment is an important problem in engineering field, and cooling methods after high temperature are important factors to be considered.\u003c/p\u003e\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eIn order to explore the damage characteristics of red sandstone after heat impact, and realize the damage assessment and quantization.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eRed sandstone specimens were heated respectively at temperature ranging from 200℃ to 700℃, and were cooled by air or water. In addition, a improved Nishihara model was used to construct a constitutive model of heat impact damage and validated using a neural network model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe test results indicate that: the peak strength of the red sandstone is bounded by 400℃, which is first increased and then decreases. And the strength of the water-cooled samples are less than that of the air-cooled samples. Both the improved Nishihara model and neural network model have high correlation coefficients and can achieve the damage assessment under different temperature and cooling rates.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThere is a temperature threshold, so that the peak strength first increases and then decreases. The cooling rate will enhance the heat impact damage and aggravate the deterioration of the physical and mechanical properties. The improved Nishihara model and neural network model can achieve damage prediction.\u003c/p\u003e","manuscriptTitle":"Study on the Mechanical Properties Equivalence of High-Temperature Red Sandstone and Neural Network Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-11 08:19:01","doi":"10.21203/rs.3.rs-5300674/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":"50cabce4-7b0c-4ce5-bbcd-b98108c007cd","owner":[],"postedDate":"November 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-19T01:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-11 08:19:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5300674","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5300674","identity":"rs-5300674","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}