Intelligent optimization-based calibration of DEM numerical parameters using an improved sparrow search algorithm

Intelligent optimization-based calibration of DEM numerical parameters using an improved sparrow search 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 Intelligent optimization-based calibration of DEM numerical parameters using an improved sparrow search algorithm YOULIN QIN, Mingyang Yu, Hong Jin, Li Yu, Mingnian Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8201691/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 Calibration of meso parameters is the foundation of discrete element method (DEM) simulations. To overcome the limitations of the traditional trial-and-error calibration method, such as its time-consuming process and poor repeatability, this study proposes an efficient DEM meso parameters calibration method based on an improved sparrow search algorithm (ISSA). The algorithm integrates multiple enhancement strategies, including improved circle chaotic mapping for population initialization, adaptive inertia weight, Lévy flight, and Cauchy–Gaussian mutation. The results demonstrate that the proposed calibration method is applicable to multi-scale models and accurately reproduces both laboratory-scale specimen results and field-scale experimental observations. The laboratory-scale DEM simulation achieved parameter calibration with a total error of less than 1% within 4.4 hours, while the field-scale DEM simulation results showed discrepancies of only 0.21% in failure depth and 0.29% in failure range compared with field observations. Furthermore, under the same accuracy conditions, the ISSA exhibits a remarkable speed advantage in meso parameters calibration, outperforming the SSA-DEM, WOA-DEM, DA-DEM, and PSO-DEM algorithms by 83.33%, 128.57%, 226.19%, and 371.42%, respectively. These results confirm that the proposed calibration method achieves automated and efficient parameter identification for DEM under the guidance of intelligent optimization algorithms. Discrete Element Method Sparrow Search Algorithm Meso parameter calibration Multi-scale simulation 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-8201691","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571654612,"identity":"725036d7-a30e-4061-ae9d-1d71d3a4d5b7","order_by":0,"name":"YOULIN 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To overcome the limitations of the traditional trial-and-error calibration method, such as its time-consuming process and poor repeatability, this study proposes an efficient DEM meso parameters calibration method based on an improved sparrow search algorithm (ISSA). The algorithm integrates multiple enhancement strategies, including improved circle chaotic mapping for population initialization, adaptive inertia weight, L\u0026eacute;vy flight, and Cauchy\u0026ndash;Gaussian mutation. The results demonstrate that the proposed calibration method is applicable to multi-scale models and accurately reproduces both laboratory-scale specimen results and field-scale experimental observations. The laboratory-scale DEM simulation achieved parameter calibration with a total error of less than 1% within 4.4 hours, while the field-scale DEM simulation results showed discrepancies of only 0.21% in failure depth and 0.29% in failure range compared with field observations. Furthermore, under the same accuracy conditions, the ISSA exhibits a remarkable speed advantage in meso parameters calibration, outperforming the SSA-DEM, WOA-DEM, DA-DEM, and PSO-DEM algorithms by 83.33%, 128.57%, 226.19%, and 371.42%, respectively. 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