Document: NRC Regulatory Guide
Document ID: a5ee4c78-1135-4bb6-8d54-e974a3402f87
Document Type: regulatory_guide
Title: An Approach for Plant-Specific, Risk-Informed Decisionmaking: Graded Quality Assurance
Source: NRC Regulatory Guide Division 1
Source URL: https://www.nrc.gov/docs/ML1221/ML12216A017.pdf
Revision Date: 2023-06
Chapter: 
Section ID: RG-1.176
CFR Part: 
CFR Title: 

Content:
ure of the model element; the other (such as risk achievement worth (RAW) or Birnbaum) illustrates the margin of safety contributed by the model element's proper operation. Other measures may be used, but at least two measures reflecting current contribution and margin contribution are needed to balance the risk insights. Importance measures represent the risk sensitivity of an individual model element. Importance measures should be compared to some quantitative guideline values. The specific values chosen as guidelines should be justified by the licensee and should reflect the estimated risk levels at the plant. All model elements characterized by importance measures greater than (or less than, as appropriate) the guidelines are identified as potentially high safety significant. Once one element is varied, the importance measures for the other elements will change. Consequently, while large or small importance measure values identify candidate high or low safety-significant model elements, final categorization is determined by an expert panel during the integrated decisionmaking. To ensure that the integrated decisionmaking is made with adequate understanding of the sensitivity of the importance results to major PRA modeling assumptions, techniques, and data, the licensee should address the technical issues associated with the use of risk importance measures to categorize SSCs discussed in Regulatory Guide 1. 174. For GQA 0 0 1.176-8 applications, a minimum of two sensitivity calcula- tions are expected; one in which recovery' actions are removed (that is, recovery probabilities set to 0.0) and one in which all common cause failures (CCFs) are removed (that is, failure probabilities set to 0.0). The studies should be performed by modifying and quantifying the original PRA logic model to minimize truncation effects. These sensitivity studies are desirable since human actions and CCF probabilities are derived from models requiring extensive interpretation and