Smart and Analytical Approaches for Assessing Rock Brittleness in Tunnel Engineering: Integrating Experimental Data with Machine Learning and Advanced Modeling Techniques

Smart and Analytical Approaches for Assessing Rock Brittleness in Tunnel Engineering: Integrating Experimental Data with Machine Learning and Advanced Modeling Techniques | 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 Smart and Analytical Approaches for Assessing Rock Brittleness in Tunnel Engineering: Integrating Experimental Data with Machine Learning and Advanced Modeling Techniques Shirin jahanmiri, Ali Aalianvari, Majid Noorian-Bidgoli, Jamal Rostami This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8807580/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 The accurate prediction of rock brittleness is a critical factor in tunnel engineering, directly influencing the safety, efficiency, and cost-effectiveness of tunneling operations. This study introduces advanced machine learning and optimization techniques to improve the precision of rock brittleness assessments, integrating experimental data with sophisticated modeling approaches. Two state-of-the-art algorithms, Artificial Rabbit Optimization (ARO) and Crayfish Optimization Algorithm (COA), were evaluated for their performance across key predictive metrics. The ARO model achieved superior results, with a Mean Absolute Error (MAE) of 0.88 and an R-squared (R²) value of 0.94, demonstrating exceptional alignment with observed brittleness values. Although the COA model exhibited a slightly higher error, with MAE of 1.23 and R² of 0.92, it still provided valuable insights into brittleness behavior. These findings highlight the transformative potential of advanced optimization algorithms in tunnel engineering, where accurate brittleness prediction is essential for preventing structural failures, reducing project costs, and optimizing machine performance. The ARO model, in particular, offers significant advantages in minimizing error and maximizing reliability, making it a powerful tool for real-world applications. By surpassing traditional predictive methods, these techniques contribute to safer, more efficient tunneling processes, underscoring their importance in modern geotechnical engineering. Physical sciences/Engineering Physical sciences/Materials science Physical sciences/Mathematics and computing Rock Brittleness Assessment Tunnel Engineering Artificial Rabbit Optimization Crayfish Optimization Algorithm Machine Learning Techniques Predictive Modeling Optimization Algorithms 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-8807580","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":591599230,"identity":"f16c04ae-d8fd-47df-962c-0337196d5dd9","order_by":0,"name":"Shirin jahanmiri","email":"","orcid":"","institution":"University of Kashan","correspondingAuthor":false,"prefix":"","firstName":"Shirin","middleName":"","lastName":"jahanmiri","suffix":""},{"id":591599231,"identity":"d6bd127c-caa6-4ef4-8a52-cb518402c68d","order_by":1,"name":"Ali Aalianvari","email":"data:image/png;base64,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","orcid":"","institution":"University of Kashan","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Aalianvari","suffix":""},{"id":591599232,"identity":"73931966-acf9-4388-bed3-1f1b573afaf0","order_by":2,"name":"Majid Noorian-Bidgoli","email":"","orcid":"","institution":"University of Kashan","correspondingAuthor":false,"prefix":"","firstName":"Majid","middleName":"","lastName":"Noorian-Bidgoli","suffix":""},{"id":591599233,"identity":"8cc56838-ca6e-49f0-928f-b672295dbd4c","order_by":3,"name":"Jamal Rostami","email":"","orcid":"","institution":"Colorado School of Mines","correspondingAuthor":false,"prefix":"","firstName":"Jamal","middleName":"","lastName":"Rostami","suffix":""}],"badges":[],"createdAt":"2026-02-06 13:24:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8807580/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8807580/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107671900,"identity":"8b4e1a33-2ef6-4804-9e71-2747232562b1","added_by":"auto","created_at":"2026-04-23 21:53:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1800045,"visible":true,"origin":"","legend":"","description":"","filename":"HOSHJAHANMIRI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8807580/v1_covered_0fdeeed7-5248-443a-bb5d-473051f3f531.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Smart and Analytical Approaches for Assessing Rock Brittleness in Tunnel Engineering: Integrating Experimental Data with Machine Learning and Advanced Modeling Techniques","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"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":"Rock Brittleness Assessment, Tunnel Engineering, Artificial Rabbit Optimization, Crayfish Optimization Algorithm, Machine Learning Techniques, Predictive Modeling, Optimization Algorithms","lastPublishedDoi":"10.21203/rs.3.rs-8807580/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8807580/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe accurate prediction of rock brittleness is a critical factor in tunnel engineering, directly influencing the safety, efficiency, and cost-effectiveness of tunneling operations. This study introduces advanced machine learning and optimization techniques to improve the precision of rock brittleness assessments, integrating experimental data with sophisticated modeling approaches. Two state-of-the-art algorithms, Artificial Rabbit Optimization (ARO) and Crayfish Optimization Algorithm (COA), were evaluated for their performance across key predictive metrics. The ARO model achieved superior results, with a Mean Absolute Error (MAE) of 0.88 and an R-squared (R\u0026sup2;) value of 0.94, demonstrating exceptional alignment with observed brittleness values. Although the COA model exhibited a slightly higher error, with MAE of 1.23 and R\u0026sup2; of 0.92, it still provided valuable insights into brittleness behavior.\u003c/p\u003e \u003cp\u003eThese findings highlight the transformative potential of advanced optimization algorithms in tunnel engineering, where accurate brittleness prediction is essential for preventing structural failures, reducing project costs, and optimizing machine performance. The ARO model, in particular, offers significant advantages in minimizing error and maximizing reliability, making it a powerful tool for real-world applications. By surpassing traditional predictive methods, these techniques contribute to safer, more efficient tunneling processes, underscoring their importance in modern geotechnical engineering.\u003c/p\u003e","manuscriptTitle":"Smart and Analytical Approaches for Assessing Rock Brittleness in Tunnel Engineering: Integrating Experimental Data with Machine Learning and Advanced Modeling Techniques","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 17:47:38","doi":"10.21203/rs.3.rs-8807580/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":"3a9b5da5-10f3-4b4a-80a9-15406b0cf55f","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62953320,"name":"Physical sciences/Engineering"},{"id":62953321,"name":"Physical sciences/Materials science"},{"id":62953322,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-23T21:53:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 17:47:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8807580","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8807580","identity":"rs-8807580","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}