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Exploration of methods for using SACADA data to estimate HEPs: Final Report

dc.contributor.authorSmith, Reuel
dc.contributor.authorMoradi, Ramin
dc.contributor.authorGroth, Katrina M.
dc.description.abstractThis report provides summary of the project "Exploration of methods for using SACADA data to estimate HEPs." The goal of the project was to conduct exploratory research on how to use the U.S. Nuclear Regulatory Commission's SACADA (Scenario, Authoring, Characterization, and Debriefing Application) database to develop an algorithm for estimating human error probabilities (HEPs). The approach used by the University of Maryland SyRRA lab uses a combination of Bayesian statistical methods and Bayesian Network models to conduct data analysis on SACADA data and to construct hybrid models informed by both data and engineering models. The end results provided various algorithms for mapping and binning SACADA data to be used within HEP estimation, and demonstrated a variety of options which create a framework for streamlined updating of HEPs as the amount and variety of SACADA data increases. This report summarizes the project's major accomplishments, and gathers the abstracts and references for the publication submissions and reports that were prepared as part of this work.en_US
dc.subjectHRA dataen_US
dc.subjectBayesian updatingen_US
dc.subjectBayesian Networksen_US
dc.titleExploration of methods for using SACADA data to estimate HEPs: Final Reporten_US
dc.typeTechnical Reporten_US
dc.relation.isAvailableAtA. James Clark School of Engineeringen_us
dc.relation.isAvailableAtMechanical Engineeringen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us

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