Acoustic emission-based intelligent identification and dual early warning for coal fatigue failure under multi-stage cyclic loading

Acoustic emission-based intelligent identification and dual early warning for coal fatigue failure under multi-stage cyclic loading | 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 Acoustic emission-based intelligent identification and dual early warning for coal fatigue failure under multi-stage cyclic loading Luyi Xing, Chao Wang, Min Liang, John X Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9250382/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Aiming at the problem of insufficient accuracy and timeliness in the early warning of coal instability under multi-stage cyclic loading, this paper proposes an intelligent recognition and dual early warning method for coal damage based on acoustic emission parameters. The multi-stage cyclic compression test of coal is carried out to obtain five characteristic parameters of acoustic emission, and the average event intensity ( ERR ) is used to characterize the sudden change characteristics of coal energy release. The Sparrow Search Algorithm (SSA) is adopted to optimize the hybrid model of Transformer and Gated Recurrent Unit (GRU), so as to realize the high-precision recognition of coal damage stages. On this basis, the early warning coefficient ( EW ) is calculated by using the Isolation Forest algorithm and the CRITIC-TODIM method, and a dual early warning system of “ preliminary prompt with ERR and comprehensive confirmation with EW ” is established. The results show that the recognition accuracy of the optimized model for the fourth damage stage is increased by 11.7% compared with the SSA-GRU model. The early warning lead time of ERR is between 60.5 s and 185.7 s, and that of EW is between 10.5 s and 101.5 s. The proposed method integrates physical mechanism and data-driven technology, which can effectively improve the accuracy and reliability of coal damage early warning under multi-stage cyclic loading, and can provide theoretical support and technical reference for the early warning of dynamic disasters in deep coal mines. Physical sciences/Energy science and technology Physical sciences/Engineering Earth and environmental sciences/Natural hazards Coal Multi-level cyclic loading Acoustic emission Damage identification Machine learning Average event intensity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviews received at journal 08 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 01 Apr, 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-9250382","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":619970927,"identity":"aa890321-17ab-4129-a18a-95771addfc21","order_by":0,"name":"Luyi Xing","email":"","orcid":"","institution":"Shandong Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"Luyi","middleName":"","lastName":"Xing","suffix":""},{"id":619970928,"identity":"59d5e3b2-7d76-49a0-bced-2fda5259dffe","order_by":1,"name":"Chao Wang","email":"data:image/png;base64,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","orcid":"","institution":"Shandong Jianzhu University","correspondingAuthor":true,"prefix":"","firstName":"Chao","middleName":"","lastName":"Wang","suffix":""},{"id":619970931,"identity":"200ef2c2-7fe8-4ca4-bb26-740371bfc0cc","order_by":2,"name":"Min Liang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Liang","suffix":""},{"id":619970936,"identity":"3e6815db-4eac-4371-9779-f36e78eacf3c","order_by":3,"name":"John X Zhao","email":"","orcid":"","institution":"Shandong Jianzhu University","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"X","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2026-03-28 06:54:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9250382/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9250382/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106728155,"identity":"080c7365-2234-41ce-9cc3-d200489d8b5e","added_by":"auto","created_at":"2026-04-12 18:42:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1637968,"visible":true,"origin":"","legend":"","description":"","filename":"Acousticemissionbasedintelligentidentificationanddualearlywarningforcoalfatiguefailureundermultistagecyclicloading.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9250382/v1_covered_a479b987-60af-43e0-8e5d-2d9015f06bfb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Acoustic emission-based intelligent identification and dual early warning for coal fatigue failure under multi-stage cyclic loading","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":"[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},"keywords":"Coal, Multi-level cyclic loading, Acoustic emission, Damage identification, Machine learning, Average event intensity","lastPublishedDoi":"10.21203/rs.3.rs-9250382/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9250382/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAiming at the problem of insufficient accuracy and timeliness in the early warning of coal instability under multi-stage cyclic loading, this paper proposes an intelligent recognition and dual early warning method for coal damage based on acoustic emission parameters. The multi-stage cyclic compression test of coal is carried out to obtain five characteristic parameters of acoustic emission, and the average event intensity (\u003cem\u003eERR\u003c/em\u003e) is used to characterize the sudden change characteristics of coal energy release. The Sparrow Search Algorithm (SSA) is adopted to optimize the hybrid model of Transformer and Gated Recurrent Unit (GRU), so as to realize the high-precision recognition of coal damage stages. On this basis, the early warning coefficient (\u003cem\u003eEW\u003c/em\u003e) is calculated by using the Isolation Forest algorithm and the CRITIC-TODIM method, and a dual early warning system of \u0026ldquo; preliminary prompt with \u003cem\u003eERR\u003c/em\u003e and comprehensive confirmation with \u003cem\u003eEW\u003c/em\u003e \u0026rdquo; is established. The results show that the recognition accuracy of the optimized model for the fourth damage stage is increased by 11.7% compared with the SSA-GRU model. The early warning lead time of \u003cem\u003eERR\u003c/em\u003e is between 60.5 s and 185.7 s, and that of \u003cem\u003eEW\u003c/em\u003e is between 10.5 s and 101.5 s. The proposed method integrates physical mechanism and data-driven technology, which can effectively improve the accuracy and reliability of coal damage early warning under multi-stage cyclic loading, and can provide theoretical support and technical reference for the early warning of dynamic disasters in deep coal mines.\u003c/p\u003e","manuscriptTitle":"Acoustic emission-based intelligent identification and dual early warning for coal fatigue failure under multi-stage cyclic loading","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 23:25:04","doi":"10.21203/rs.3.rs-9250382/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-20T09:51:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T17:03:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T02:11:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150988125686233769917759865775016282770","date":"2026-04-07T02:51:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210290132069753282410466400262105267555","date":"2026-04-07T00:07:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T23:00:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T15:16:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T01:08:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-02T01:01:20+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":"7fce69b0-e037-413a-a760-cebf67a16b04","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65976244,"name":"Physical sciences/Energy science and technology"},{"id":65976245,"name":"Physical sciences/Engineering"},{"id":65976246,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2026-05-11T13:18:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 23:25:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9250382","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9250382","identity":"rs-9250382","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}