Random field-based probabilistic support for the assessment of the electrical conductivity of industrial floors and their operational reliability in time

Random field-based probabilistic support for the assessment of the electrical conductivity of industrial floors and their operational reliability in time | 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 Random field-based probabilistic support for the assessment of the electrical conductivity of industrial floors and their operational reliability in time Maciej Niedostatkiewicz, Karol Winkelmann, Sylwia Świątek-Żołyńska, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8052329/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract In modern engineering, electro-resistant flooring plays a crucial role in preventing electrostatic discharges that may pose safety hazards to people or can damage sensitive electronic equipment. The electrical resistance of industrial floors, characterized primarily by the electric leakage resistance parameter, is often highly variable due to localized material imperfections and temporal fluctuations. Hence, enforcing strict regulatory requirements to ensure long-term reliability and serviceability of such flooring necessitates systematic in-situ testing. While these measurements provide important insights into the current state of the industrial floor, they also suffer from proprietary limitations, environmental variability, and instrument sensitivity. These issues may lead to situations where a single set of measurements fails to represent the overall performance of the flooring. Furthermore, a high economic cost of frequent testing must also be addressed. To account for these challenges, a probabilistic approach is introduced, enhancing the possibility of interpretation of the variability in the in-situ results and enabling long-term predictions of the flooring operational reliability. The methodology combines annual real-life measurements with simplified stochastic updating models. Representative correlated random fields are generated in the process, with Monte Carlo simulations and Cornell linear reliability index approach used to evaluate the failure probability and reliability change over time. By integrating real-live tests of the flooring with their simulated random field-based numerical counterparts, the proposed approach refines prediction accuracy and helps engineers make more informed maintenance decisions. Physical sciences/Engineering Physical sciences/Materials science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Jan, 2026 Reviews received at journal 08 Jan, 2026 Reviewers agreed at journal 30 Dec, 2025 Reviews received at journal 17 Dec, 2025 Reviews received at journal 10 Dec, 2025 Reviewers agreed at journal 30 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 24 Nov, 2025 Editor assigned by journal 24 Nov, 2025 Editor invited by journal 24 Nov, 2025 Submission checks completed at journal 13 Nov, 2025 First submitted to journal 13 Nov, 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-8052329","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":551827043,"identity":"801a1e59-e768-4905-9e38-c4b4a16204b5","order_by":0,"name":"Maciej Niedostatkiewicz","email":"","orcid":"","institution":"Gdańsk University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Maciej","middleName":"","lastName":"Niedostatkiewicz","suffix":""},{"id":551827044,"identity":"fdde4209-9c26-4ec4-a6d3-2ab1168f47d8","order_by":1,"name":"Karol 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