Non-Destructive Testing of Steel-Lined Concrete Structure Using Multiple Agents With Deep Prior

Non-Destructive Testing of Steel-Lined Concrete Structure Using Multiple Agents With Deep Prior | 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 Non-Destructive Testing of Steel-Lined Concrete Structure Using Multiple Agents With Deep Prior Abdulrahman M. Alanazi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6799907/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Non-destructive testing (NDT) is a cornerstone of structural integrity assessment that enables internal evaluation of materials without inflicting damage. Among various imaging methods, ultrasonic model-based iterative reconstruction (UMBIR) has gained attention for its ability to enhance ultrasonic imaging by incorporating physical and statistical priors. Notably, UMBIR and its extension, Multi-Frequency UMBIR (MF-UMBIR), have shown improved accuracy over traditional techniques. However, their performance in highly complex structures. In this paper, we propose Deep-MACE, a novel reconstruction framework that integrates multi-frequency forward model agents with a deep learning prior using the consensus equilibrium formulation. By combining data consistency across different excitation frequencies with the expressive power of a learned U-Net prior, Deep-MACE enables high-fidelity imaging in acoustically heterogeneous environments. Experimental results demonstrate that both UMBIR and MF-UMBIR suffer from limitations in defect visibility and robustness when applied to steel-lined concrete structures. In contrast, Deep-MACE consistently produces clearer reconstructions, successfully identifying all internal rebars with fewer artifacts and improved spatial resolution. These results highlight the potential of integrating deep priors into multi-agent frameworks for advanced ultrasonic NDT. Physical sciences/Materials science/Techniques and instrumentation/Imaging techniques Physical sciences/Engineering/Electrical and electronic engineering Non-destructive testing ultrasonic imaging model-based inversion UMBIR deep learning consensus equilibrium multi-frequency recon Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 15 Jul, 2025 Reviews received at journal 04 Jul, 2025 Reviews received at journal 28 Jun, 2025 Reviewers agreed at journal 22 Jun, 2025 Reviewers agreed at journal 21 Jun, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviewers agreed at journal 18 Jun, 2025 Reviewers invited by journal 16 Jun, 2025 Editor invited by journal 12 Jun, 2025 Editor assigned by journal 05 Jun, 2025 Submission checks completed at journal 04 Jun, 2025 First submitted to journal 02 Jun, 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-6799907","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":472741211,"identity":"d8b8c69e-e610-4943-9bc4-46bff49f29b0","order_by":0,"name":"Abdulrahman M. 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