BibTeX

@inproceedings{GrothPSAM2018HRA,
title = {A framework for using SACADA to enhance the qualitative and quantitative basis of HRA},
author = {Katrina M Groth},
url = {http://psam14.org/proceedings/paper/paper_412_1.pdf},
year = {2018},
date = {2018-09-16},
booktitle = {Proceedings of the 14th Probabilistic Safety Assessment and Management Conference (PSAM 14)},
address = {Los Angeles, CA},
keywords = {Bayesian Networks, Bayesian updating, HRA data, SACADA},
pubstate = {published},
tppubtype = {inproceedings}
}

Abstract

The purpose of this research is to explore ways to use Bayesian methods with data from the US Nuclear Regulatory Commission’s (NRC) Scenario, Authoring, Characterization, and Debriefing Application (SACADA) system. SACADA is a database designed to enable collection of nuclear power plant (NPP) control room simulator and crew training data to improve both operator training and human reliability analysis (HRA). This paper presents a framework to use SACADA data and causal modeling to enhance the qualitative and quantitative aspects of HRA. The framework is a multi-faceted approach involving causal models as well as multiple sources and types of data. Elements of the framework include a comprehensive set of performance influencing factors, crew
failure modes, Bayesian Network causal models, Bayesian parameter updating, and temporal modeling. This paper also outlines a path forward for developing the framework to enhancing the technical basis of HRA and enabling streamlined use of SACADA data as the volume and variety of data increases.