Non-Destructive Characterization and AI-Based Damage Prediction in Ceresit Mortar Using Experimental Modal Analysis and Hybrid SAINT-HBA Model

Non-Destructive Characterization and AI-Based Damage Prediction in Ceresit Mortar Using Experimental Modal Analysis and Hybrid SAINT-HBA Model | 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 Non-Destructive Characterization and AI-Based Damage Prediction in Ceresit Mortar Using Experimental Modal Analysis and Hybrid SAINT-HBA Model Aya bendada, Abdelwahhab Khatir, Djillali Boutchicha, Samir Khatir, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8013437/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 In the present study, mechanical behavior of Ceresit 20 mortar is investigated through non-destructive experimental modal testing on a mortar beam. The objective was to determine the Young's modulus (E) in three principal directions longitudinal, transverse, and vertical in terms of bending frequencies. Torsion mode data was utilized in order to calculate the shear modulus (G) and to calculate Poisson's ratio (ν). A finite element model was used for validation, with very good correlation with experiment. Besides, a new Machine Learning (ML) model, Self-Attention Interpretable Neural Transformer optimized by Honey Badger Algorithm (SAINT-HBA), was introduced to predict crack depth of mortar beams. Utilizing bending and torsion frequencies of failed specimens, the model predicted single and multiple crack scenarios very well. SAINT-HBA outperformed hybrid SAINT models that used Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Ecosystem-based Optimization (AEO) and was found to be useful for structural health monitoring applications. Ceresit 20 mortar Non-distructive testing Structural Health Monitoring Machine Learning SAINT-HBA algorithm Vibration analysis Finite Element Method 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-8013437","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":567092606,"identity":"f55022c0-e71f-4269-9d64-6512769f8298","order_by":0,"name":"Aya bendada","email":"","orcid":"","institution":"University of Sciences and Technologies Mohamed Boudiaf of Oran (USTO-MB), EL-MNAOUER","correspondingAuthor":false,"prefix":"","firstName":"Aya","middleName":"","lastName":"bendada","suffix":""},{"id":567092607,"identity":"3713f785-0b1f-4fd4-81f1-6b3aabe01269","order_by":1,"name":"Abdelwahhab 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