AMRBF-SS: Subset Simulation with Active Learning and Multiple Kernels Radial Basis Function for Small Failure Probability Prediction

AMRBF-SS: Subset Simulation with Active Learning and Multiple Kernels Radial Basis Function for Small Failure Probability Prediction | 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 AMRBF-SS: Subset Simulation with Active Learning and Multiple Kernels Radial Basis Function for Small Failure Probability Prediction Changle Peng, Cheng Chen, Tong Guo, Weijie Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4156800/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Combining surrogate models with simulation methods is an effective way to address failure probability problems involving time-consuming computational models in structural reliability analysis. The radial basis function (RBF) has been widely used in the context of uncertainty quantification owing to its flexibility, nonlinearity, computational efficiency, and ability to handle high-dimensional data. Multiple RBF kernel functions are integrated in this study with subset simulation (SS) to formulate the proposed AMRBF-SS method to efficiently solve the problems of small failures. The local uncertainty of the prediction is estimated and integrated into to formulate an active learning function. Various numerical and practical examples are considered to verify the accuracy and efficiency of the proposed method. The results show that the proposed AMRBF-SS method provides an effective and efficient technique that can solve high-dimensional small-probability problems with similar accuracy levels but fewer evaluation times than other existing methods. Active learning Radial basis function Subset simulation Small-failure High dimensional Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 May, 2024 Reviews received at journal 30 Apr, 2024 Reviewers agreed at journal 17 Apr, 2024 Reviewers invited by journal 17 Apr, 2024 Submission checks completed at journal 08 Apr, 2024 Editor assigned by journal 08 Apr, 2024 First submitted to journal 24 Mar, 2024 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. 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