Research on the Mechanical Properties of Xiyu conglomerate Based on Deep Learning Digital Image Analysis | 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 the Mechanical Properties of Xiyu conglomerate Based on Deep Learning Digital Image Analysis Yulu Zhang, chong Shi, Long Jiang, Liang Liu, wei Qiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7645698/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 Xiyu conglomerate is a special geological body that lies between rock and soil. Its mechanical properties are determined by the microscopic characteristics of the framework particles. How to identify its microscopic structure and evaluate its impact on mechanical properties has attracted much attention in the geotechnical field. In this paper, based on the typical Xiyu conglomerate of the Momoke Key Water Control Project, conducted indoor experiments. It introduced the deep learning digital image recognition method to identify its fabric characteristics, and used particle flow numerical simulation to analyze the Mesoscopic Mechanical Properties. It also explored the effects of cement content, cement composition, and stone content on the strength characteristics of the Xiyu conglomerate. The results show that the higher the content of calcareous cement, the more closely the particles are interlocked, and the greater their density will be. When the stone content is fixed at 50%, as the proportion of calcareous cementation increases, the internal friction angle of the Western Regions conglomerate keeps increasing. The cohesion gradually increases and then decreases. When the content of calcareous cementation increases from 25% to 75%, the internal friction angle rises from 16.3°to 32.1°. When the proportion of cementing material is fixed at 50%, as the content of stone increases, the squeezing and biting effects between the boulders become significant, and the internal friction angle and cohesion also increase significantly. The research results can provide a reference for engineering construction in the Xiyu conglomerate area. Xiyu conglomerate Deep Learning for Digital Image Recognition Fabric characteristics Mesoscopic Numerical Simulation Strength Properties 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-7645698","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525231647,"identity":"f0af8b07-03c2-49a0-a2ac-1e4276081cb2","order_by":0,"name":"Yulu Zhang","email":"","orcid":"","institution":"Xinjiang Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yulu","middleName":"","lastName":"Zhang","suffix":""},{"id":525231648,"identity":"2389ef7c-d7fa-4698-919f-4b39f6b86ad4","order_by":1,"name":"chong 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