Supervised machine learning for identification of glass properties: Towards structural Stability and performance

Supervised machine learning for identification of glass properties: Towards structural Stability and performance | 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 Supervised machine learning for identification of glass properties: Towards structural Stability and performance Abba Bashir, Adagba. T Terlumun, Salim Idris Malami, M. M Jibril, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3851231/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 Classical and physics-based modelling is a basic way to describe how physical processes work, but it has many problems. For example, it uses a lot of computing power, takes a long time, and can't show how random and complicated processes work in glass science and engineering. On the other hand, machine learning (ML) models have been shown to get around this problem, especially when a precise and reliable estimate is needed. In this study, neural network (NN), adaptive neuro fuzzy inference system (ANFIS), k-nearest neighbors (KNN), and robust linear regression (RLR) models were used to simulate the spring constant (K) at the junction of structural glass plates. The data from the experiment, which included axial load (N) and four different displacements (mm) and was collected in a total of 2879 cases, was pre-processed and split into 70% calibration and 30% verification. After that, sensitivity analysis was done, and 6 different model combinations (M1 through M6) were made. Based on the results of three performance evaluation criteria (R2, RMSE, and R), the ML model did well and could be trusted to estimate K. The ANN-M5, ANN-M6, ANFIS-M5, ANFIS-M6, KNN-M5, KNN-M6, RLR-M5, and RLR-M6 models, on the other hand, did 0.1 percent better than the rest. The model follows the latest best practices in machine learning and makes it possible to do experiments on low-power edge computing devices with minimal cost. KNN-M5 and KNN-M6 were the best models in terms of RMSE, but the confidence interval values showed that they were better than the best model (95%). Artificial Intelligence Glass science spring constant axial load Glass Panel Structure machine learning 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-3851231","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266528168,"identity":"14c3c5ac-be5a-418b-b990-b3b52730017f","order_by":0,"name":"Abba Bashir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYDACCR4GZiCVwMbAfOBAgoENkM3YeIBILWyJDx5UpIG0NBCnhYGBx9jwwZnDYEG8Wvhn9x78XFBTl8cnfcBMIrHtvN3a9sNAW2psonFacudcsvSMY4eL2fgS0oBabidvO5MI1HIsLbcBl54bOQbSPGwHEtt4GI6BtZgdAGphbDiMU4v8jRzj3zz/6oBaGNuAWs4lm51/iF+LwY0cM2neNmagFmZmg4QzB+zMbhCwxfDOuTTrmX2HgVrYGB8kVCQnmN0A2pKAxy9yt3sP3y74Vpc4v4f/w8EfBnb2ZufTHz74UGOD2/voIBGsMoFY5SBgT4riUTAKRsEoGBkAAHcvZ/wYqLL/AAAAAElFTkSuQmCC","orcid":"","institution":"Federal University Dutsinma","correspondingAuthor":true,"prefix":"","firstName":"Abba","middleName":"","lastName":"Bashir","suffix":""},{"id":266528169,"identity":"0def0462-d0a2-42e3-a2ca-76598aed8806","order_by":1,"name":"Adagba. 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