{"paper_id":"43bd0bab-e375-4833-8246-3fd1e657626f","body_text":"Conversion, Prediction, and Application of Strength Parameters for Grouted Reconstructed Rock Mass | 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 Conversion, Prediction, and Application of Strength Parameters for Grouted Reconstructed Rock Mass Ermeng Zhang, Lang Liu, Yanchun Xu, Qiang Wu, Yu Fei, Yabin Lin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4666355/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 5 You are reading this latest preprint version Abstract Ultrasonic detection has emerged as a rapid method for acquiring rock mass sound velocity and converting it into an elastic modulus parameter, a pivotal technique for investigating the in-situ mechanical properties of rock masses. Despite its significance, accurately deducing rock mass strength from elastic modulus remains a formidable challenge and a pressing issue in the realm of protorock parameter research. This study introduces an innovative artificial intelligence-driven methodology for transforming elastic modulus and strength parameters specific to coal measures through rigorous data analysis and experimental validation. By integrating two illustrative engineering cases, we explore the complexities of water inrush and floor heave issues encountered in tunnels traversing fault zones. The novel strength parameter calculation approach is benchmarked against previous studies, highlighting its superior advantages in terms of effectiveness and applicability. In essence, this research offers a comprehensive framework and practical workflow for translating in-situ acoustic parameter-derived elastic modulus into rock mass strength, serving as a valuable resource for future endeavors in mine water control research. Physical sciences/Engineering/Civil engineering Earth and environmental sciences/Natural hazards Reconstructed Rock Mass elastic modulus rock mass strength artificial intelligence-driven methodology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Full Text Additional Declarations No competing interests reported. Tables 1 to 6 are available in the Supplementary Files section Supplementary Files Table1.docx Table2.docx Table3.docx Table4.docx Table5.docx Table6.docx Cite Share Download PDF Status: Published Journal Publication published 25 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 22 Jul, 2024 Editor assigned by journal 07 Jul, 2024 Editor invited by journal 07 Jul, 2024 Submission checks completed at journal 03 Jul, 2024 First submitted to journal 01 Jul, 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. 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-4666355\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":330135523,\"identity\":\"82a7c9f3-f869-42e2-a0c1-dbf7ea934ff2\",\"order_by\":0,\"name\":\"Ermeng 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