Artificial Neural Network: A tool for Rapid Quantitative Elemental Analysis Using Neutron Activation Analysis

Artificial Neural Network: A tool for Rapid Quantitative Elemental Analysis Using Neutron Activation 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 Artificial Neural Network: A tool for Rapid Quantitative Elemental Analysis Using Neutron Activation Analysis M. E. Medhat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9440511/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 This paper presents a methodology for rapid quantitative elemental analysis using Neutron Activation Analysis (NAA) coupled with an Artificial Neural Network (ANN). A three-layer feed-forward ANN with back-propagation algorithm was developed to determine concentrations of long-lived activation products (Co, Cs, Eu, Fe, Hf, Sb, Sc, Ta, Tb, Ce) relevant to nuclear reactor shielding decommissioning. The methodology is demonstrated using simulated gamma-ray spectral data generated from the activation equation based on typical cement composition ranges reported in literature. The optimized ANN architecture (4 input neurons; 1 hidden layer with 4 neurons using tanh activation; 1 output neuron) achieved a correlation coefficient of 0.991 between input features and predicted concentrations. Mean relative errors ranged from 3.2% to 6.2% across all elements. The proposed method eliminates the need for matched elemental standards and reduces analysis time by approximately 80% compared to conventional relative NAA. This methodology provides a foundation for rapid, multi-element analysis in nuclear decommissioning applications, with experimental validation planned for future work. Neutron Activation Analysis Artificial Neural Networks Gamma-ray spectrometry Reactor decommissioning Cement analysis 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-9440511","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628206973,"identity":"d73f6165-ee15-499f-b8f0-97d925a788d4","order_by":0,"name":"M. E. 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