Research on Rock Strength Prediction Model Based on Machine Learning Algorithm

Research on Rock Strength Prediction Model Based on Machine Learning Algorithm | 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 Research on Rock Strength Prediction Model Based on Machine Learning Algorithm Xiang Ding, Mengyun Dong, Wanqing Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5049103/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract The compressive strength of rocks is one of its mechanical characteristics. It has been a difficult problem to predict rock compressive strength conveniently and efficiently, and to solve the limitations of traditional rock compressive strength tests such as high cost, long time consumption, and reliability assurance. In this study, a data set containing 1774 groups of rock compressive strength test data was constructed through file retrieval, including 9 input parameters: rock type, temperature, confining pressure, dimension of specimen, shape of specimen, and experimental method. Eight supervised learning algorithms were used to learn the rock compressive strength test data, and eight rock compressive strength prediction models considering multiple factors were established to obtain a better method of predicting rock compressive strength. By selecting different features, the optimal feature combination for predicting rock compressive strength was obtained, and the optimal parameters for different models were obtained through the Sparrow Search Algorithm (SSA). Finally, four regression evaluation indicators, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²), were used to evaluate the predictive performance of the established regression models. The results showed that the best-trained model had a MAPE as low as 3.61%, MAE as low as 9.19 MPa, and R² as high as 0.995. It is noteworthy that AdaBoost was found to be the best model for predicting rock compressive strength. This study presents a significant advancement in the field by demonstrating the effectiveness of machine learning algorithms in this context, which have not been extensively applied to rock compressive strength predictions. The findings suggest that these models can offer substantial improvements over traditional methods, not only in accuracy but also in operational efficiency. This research is important for geotechnical engineering, as accurate rock strength predictions are critical for the design and stability assessments of construction projects, ultimately contributing to safer and more cost-effective engineering solutions. Machine learning Rock mechanics Feature selection The compressive strength of rocks Triaxial tests Full Text Additional Declarations No competing interests reported. Supplementary Files data.xls Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Sep, 2024 Reviews received at journal 26 Sep, 2024 Reviews received at journal 24 Sep, 2024 Reviews received at journal 23 Sep, 2024 Reviewers agreed at journal 23 Sep, 2024 Reviews received at journal 22 Sep, 2024 Reviewers agreed at journal 22 Sep, 2024 Reviewers agreed at journal 22 Sep, 2024 Reviewers agreed at journal 20 Sep, 2024 Reviewers agreed at journal 20 Sep, 2024 Reviewers agreed at journal 20 Sep, 2024 Reviewers invited by journal 20 Sep, 2024 Editor assigned by journal 19 Sep, 2024 Submission checks completed at journal 16 Sep, 2024 First submitted to journal 07 Sep, 2024 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. 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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-5049103","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":360283992,"identity":"994c2a11-2e01-4702-ab4d-98c957090fb5","order_by":0,"name":"Xiang Ding","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Ding","suffix":""},{"id":360283993,"identity":"15c95d15-6650-4467-be10-3dd766b6009c","order_by":1,"name":"Mengyun Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACxobzjx98+GHDw0+0FubGM2yGM3vSZCQbiNXC3nyGQZqH7bCNwQFitfC2nT1gzMOTxmN8PHkDw4+KbYS1SPacS3g4x8KGx+zMswLGnjO3CWsxnHHAwOAN0BazGzkGzIxtRGixv//AQALoFx7jGcRqYWw4YyAJ0mIgQbyWY2mgQOaRAPrlIFF+YWw4fBgUlfb87ckbH/yoIEILEkggPmoQWkjVMQpGwSgYBSMEAADcukGvy3JM3AAAAABJRU5ErkJggg==","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Mengyun","middleName":"","lastName":"Dong","suffix":""},{"id":360283994,"identity":"38d19cd0-74c9-412c-ad26-d57755c66db1","order_by":2,"name":"Wanqing Shen","email":"","orcid":"","institution":"CNRS FR 2016","correspondingAuthor":false,"prefix":"","firstName":"Wanqing","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2024-09-07 13:23:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5049103/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5049103/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69896252,"identity":"4cff1ea2-dd35-4318-935a-d58b935dfa34","added_by":"auto","created_at":"2024-11-26 11:13:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":877292,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5049103/v1_covered_e73d30d0-4ab7-476d-88e4-d763be3226c8.pdf"},{"id":69894856,"identity":"1a288a95-17ee-4686-9899-31b4dcb89f00","added_by":"auto","created_at":"2024-11-26 11:05:31","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":210432,"visible":true,"origin":"","legend":"","description":"","filename":"data.xls","url":"https://assets-eu.researchsquare.com/files/rs-5049103/v1/a2272fa625ff951939a2ad34.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Rock Strength Prediction Model Based on Machine Learning Algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Machine learning, Rock mechanics, Feature selection, The compressive strength of rocks, Triaxial tests","lastPublishedDoi":"10.21203/rs.3.rs-5049103/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5049103/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe compressive strength of rocks is one of its mechanical characteristics. It has been a difficult problem to predict rock compressive strength conveniently and efficiently, and to solve the limitations of traditional rock compressive strength tests such as high cost, long time consumption, and reliability assurance. In this study, a data set containing 1774 groups of rock compressive strength test data was constructed through file retrieval, including 9 input parameters: rock type, temperature, confining pressure, dimension of specimen, shape of specimen, and experimental method. Eight supervised learning algorithms were used to learn the rock compressive strength test data, and eight rock compressive strength prediction models considering multiple factors were established to obtain a better method of predicting rock compressive strength. By selecting different features, the optimal feature combination for predicting rock compressive strength was obtained, and the optimal parameters for different models were obtained through the Sparrow Search Algorithm (SSA). Finally, four regression evaluation indicators, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R\u0026sup2;), were used to evaluate the predictive performance of the established regression models. The results showed that the best-trained model had a MAPE as low as 3.61%, MAE as low as 9.19 MPa, and R\u0026sup2; as high as 0.995. 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This research is important for geotechnical engineering, as accurate rock strength predictions are critical for the design and stability assessments of construction projects, ultimately contributing to safer and more cost-effective engineering solutions.\u003c/p\u003e","manuscriptTitle":"Research on Rock Strength Prediction Model Based on Machine Learning Algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-26 11:05:26","doi":"10.21203/rs.3.rs-5049103/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-29T16:26:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-26T09:56:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-24T10:46:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-23T19:49:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64508911123867401690692375338742311089","date":"2024-09-23T05:39:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-22T16:15:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86999065032511927751497286074194232401","date":"2024-09-22T14:46:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204803745541012556562206095202274169254","date":"2024-09-22T13:24:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321025574224192244316403435801776150908","date":"2024-09-20T19:32:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14051050351534461570825401877414849234","date":"2024-09-20T14:15:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151566297424152951858546412913948668073","date":"2024-09-20T13:56:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-20T12:57:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-19T06:17:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-16T12:24:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2024-09-07T13:16:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"76c14e40-237c-472e-b564-178d016d05ad","owner":[],"postedDate":"November 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-11-26T11:05:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-26 11:05:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5049103","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5049103","identity":"rs-5049103","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}