Incorporating a-priori knowledge into Convolutional Neural Networks for Impact Echo frequency estimation

Incorporating a-priori knowledge into Convolutional Neural Networks for Impact Echo frequency estimation | 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 Incorporating a-priori knowledge into Convolutional Neural Networks for Impact Echo frequency estimation Fabian Dethof, Sylvia Keßler This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6863929/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Dec, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted 11 You are reading this latest preprint version Abstract Manual evaluation and interpretation of Impact-Echo (IE) data is often labor-intensive and time-consuming, motivating the growing interest in applying machine learning (ML) techniques to this non-destructive testing (NDT) method. However, the scarcity of labeled datasets limits the generalizability of ML models to new, unseen data. This study investigates strategies to integrate a-priori knowledge into Convolutional Neural Networks (CNNs) for improved prediction of the S1 Lamb wave frequency from IE signals. To this end, time–frequency representations of IE signals, derived using the Short-Time Fourier Transform (STFT), are used as model inputs. A-priori knowledge is introduced in the form of initial frequency estimates obtained through manual evaluation. Additionally, transfer learning is employed to enrich the limited measurement dataset with data from 2D numerical simulations. The results demonstrate that, although training loss curves remain similar across models, incorporating additional information significantly enhances performance on unseen datasets. Furthermore, pre-training with simulation data accelerates convergence during early fine-tuning stages. The highest predictive accuracy was achieved when the initial guess was directly embedded into the loss function. impact echo machine learning convolutional neural network a-priori knowledge Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Dec, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted Editorial decision: Revision requested 16 Nov, 2025 Reviews received at journal 15 Nov, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviews received at journal 07 Oct, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers agreed at journal 14 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers invited by journal 13 Jul, 2025 Editor assigned by journal 11 Jul, 2025 Submission checks completed at journal 11 Jun, 2025 First submitted to journal 10 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. 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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-6863929","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485782868,"identity":"34fdf830-f445-432b-9527-570a07c49a9e","order_by":0,"name":"Fabian Dethof","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIie3OMWsCMRTA8XfLuaTnGihHv0LKgZN4XyWH0C6nozh0OBCyiauC+Blukm5NeHDT6ScoeJOTLXXsUNqkrQ5CTkeH/Jckwy/vAbhcV1gjIyD16WVAAIZDcyc+gG8nRB6Ir0lZXkjgSFbiEtJYKfn5DOHkbqwqtXiNmbwpKhi07YT0uRqXEM1E0GVquU1yGTwyWD9YSQyp/lZAkhekdbtfImebXYt6Au1TmjumvjR50YSqOerFiCHfdkJThr9TfEMy9PI/ImvIO8dQ0GhapBGTBSYzQ/i6W7NYD/dvoh1ORuV9JZ8wDgz5GHSs5D968ubngMvlcrlq+wEXHVmrMOpg5gAAAABJRU5ErkJggg==","orcid":"","institution":"Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg","correspondingAuthor":true,"prefix":"","firstName":"Fabian","middleName":"","lastName":"Dethof","suffix":""},{"id":485782869,"identity":"c81a6dd4-2279-422a-874f-06a517a6d255","order_by":1,"name":"Sylvia Keßler","email":"","orcid":"","institution":"Helmut-Schmidt-University / University of the Federal Armed Forces Hamburg","correspondingAuthor":false,"prefix":"","firstName":"Sylvia","middleName":"","lastName":"Keßler","suffix":""}],"badges":[],"createdAt":"2025-06-10 14:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6863929/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6863929/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10921-025-01312-8","type":"published","date":"2025-12-22T15:57:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99172277,"identity":"4626ce57-fd94-4844-8871-fe26558e6af3","added_by":"auto","created_at":"2025-12-29 16:07:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1994764,"visible":true,"origin":"","legend":"","description":"","filename":"JONEMLIEpaperDethofKessler.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6863929/v1_covered_711081de-97ea-4000-93c3-c30b68f71f27.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Incorporating a-priori knowledge into Convolutional Neural Networks for Impact Echo frequency estimation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-nondestructive-evaluation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jone","sideBox":"Learn more about [Journal of Nondestructive Evaluation](http://link.springer.com/journal/10921)","snPcode":"10921","submissionUrl":"https://submission.nature.com/new-submission/10921/3","title":"Journal of Nondestructive Evaluation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"impact echo, machine learning, convolutional neural network, a-priori knowledge","lastPublishedDoi":"10.21203/rs.3.rs-6863929/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6863929/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eManual evaluation and interpretation of Impact-Echo (IE) data is often labor-intensive and time-consuming, motivating the growing interest in applying machine learning (ML) techniques to this non-destructive testing (NDT) method. 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