Analysis of Key Geological Structures and Rockburst Prediction Method

Analysis of Key Geological Structures and Rockburst Prediction Method | 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 Analysis of Key Geological Structures and Rockburst Prediction Method Chunchi Ma, Yang Yuan, Xiang Ji, Feng Peng, Ziquan Chen, Hang Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5646882/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 May, 2025 Read the published version in Scientific Reports → Version 1 posted 6 You are reading this latest preprint version Abstract Rockburst disasters generally occur within specific geological structures. Consequently, predicting and evaluating the potential rockbursts necessitates more focuses on the “geological carrier”. Considering the geological structure effects of rockbursts, a novel prediction method based on the key geological structures was proposed by combining numerical simulation with neural network. This study systematically investigated typical geological structures and geomechanical modes of rockbursts. An evaluation index for the relative energy release effect of rockbursts was established through the numerical simulation, which can be adopted to analyze the sensitivity of the key structural parameters. Subsequently, a surrogate model was developed using GA-BP neural network combined with Latin hypercube sampling to accurately represent the relationship between the key structural parameters and rockburst effects. Using the rockburst intensity classification scheme and the Monte Carlo method, the samples within the dangerous range of rockbursts were identified. By analyzing the rockburst sample points of various grades, the confidence intervals for the key structural parameters were interpreted, with the prediction method developed for identifying the key geological structures. This innovative method facilitated the rapid "dictionary-style" prediction of rockbursts in underground engineering. It comprehensively considered the impacts of geological structures on rockbursts and could offer a new pathway for precise rockburst disaster prediction. Physical sciences/Engineering/Civil engineering Earth and environmental sciences/Solid earth sciences/Petrology Rockburst prediction Geological structure effect Relative energy release effect Numerical simulation Confidence interval Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 22 Apr, 2025 Reviews received at journal 22 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers invited by journal 22 Apr, 2025 Submission checks completed at journal 21 Apr, 2025 First submitted to journal 17 Apr, 2025 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-5646882","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":446307083,"identity":"4294f326-fc37-47ce-8233-8cfc2cf90a78","order_by":0,"name":"Chunchi 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