AI-Driven Optimization of Helmet Material Design: Mitigating Traumatic Axonal Injuries through Innovative Constitutive Law Enhancement

AI-Driven Optimization of Helmet Material Design: Mitigating Traumatic Axonal Injuries through Innovative Constitutive Law Enhancement | 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 AI-Driven Optimization of Helmet Material Design: Mitigating Traumatic Axonal Injuries through Innovative Constitutive Law Enhancement Dominique Pioletti, Vincent Varanges, Pezhman Eghbali, Naser Nasrollahzadeh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3791451/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Feb, 2025 Read the published version in Communications Engineering → Version 1 posted You are reading this latest preprint version Abstract Sports helmets do not provide full protection against brain injuries. Our study aims to improve helmet liner efficiency by employing a novel approach that optimizes their properties. By exploiting a finite element model that simulates impacts, we developed deep learning models that predict the peak kinematics of a dummy head protected by various liner materials. The models exhibited a remarkable correlation coefficient of 0.99 within the training dataset, highlighting their predictive ability. Deep learning-based material optimization predicts a significant reduction in the risk of traumatic axonal injuries for impact energy ranging from 250 to 450 Joules. This result emphasizes the effectiveness of a sophisticated material design to mitigate sport-related brain injury risks. This research introduces promising avenues for optimizing helmet designs to enhance their protective capabilities. Physical sciences/Engineering/Mechanical engineering Physical sciences/Engineering/Biomedical engineering Physical sciences/Materials science/Theory and computation/Computational methods Traumatic brain injury material design brain injury metric deep learning injury risk numerical analysis traumatic axonal injury Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 16 Feb, 2025 Read the published version in Communications Engineering → 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. 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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-3791451","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267138136,"identity":"76cf16d6-aca3-42de-b7fd-618d686e1c71","order_by":0,"name":"Dominique 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