The use of empirical data sources in HRA

Hallbert, Bruce; Gertman, David I.; Lois, Erasmia; Marble, Julie L.; Blackman, Harold S.; Byers, James C.

doi:10.1016/j.ress.2003.09.005

Cited by 27 publications

(21 citation statements)

References 3 publications

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“…Another implication is that the interactions among PIFs may be difficult to be interpreted and left to interpreters' subjectivity, because (1) the aggregated effects are not simply the addition or multiplication of individual PIF effects, or (2) the unavoidable confounds make it impossible to separate individual PIF effects. Under such circumstances, it may be useful and meaningful to interpret their aggregated effects as a single context -combining the confounding PIFs as a single unit rather than treating them separately [7]. Specific to IDHEAS, each DT consists of approximately three or four PIFs.…”

Section: Discussionmentioning

confidence: 99%

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Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

Liao

Groth

Marie

2015

Reliability Engineering & System Safety

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This article documents an exploratory study for collecting and using human performance data to inform human error probability (HEP) estimates for a new human reliability analysis (HRA) method, the IntegrateD Human Event Analysis System (IDHEAS). The method was based on cognitive models and mechanisms underlying human behaviour and employs a framework of 14 crew failure modes (CFMs) to represent human failures typical for human performance in nuclear power plant (NPP) internal, at-power events [1]. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts. Data needs for IDHEAS quantification are discussed. Then, the data collection framework and process is described and how the collected data were used to inform HEP estimation is illustrated with two examples. Next, five major technical challenges are identified for leveraging human performance data for IDHEAS quantification. These challenges reflect the data needs specific to IDHEAS. More importantly, they also represent the general issues with current human performance data and can provide insight for a path forward to support HRA data collection, use, and exchange for HRA method development, implementation, and validation.

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Section: Discussionmentioning

confidence: 99%

“…Therefore, these data sources complement each other with a faithful representation of certain information at varying levels of detail. To make good use of human performance data for HRA, it is important to identify what information could most appropriately, efficiently, and effectively be obtained from each of the data sources [7].…”

Section: Introductionmentioning

confidence: 99%

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

Liao

Groth

Marie

2015

Reliability Engineering & System Safety

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“…Such data will continue to strengthen our ability to understand the characteristics of situations that can lead to unsafe human actions and provide an additional basis for estimating the likelihood of those unsafe actions. The USNRC is currently supporting several national and international efforts along these lines, including work at the Halden Research Project in Norway using state-of-the-art nuclear power plant simulators, the international Organization for Economic Cooperation and Development Nuclear Energy Agency (OECD/NEA) efforts to develop an international database of nuclear power plants events, and work by Idaho National Laboratory to build a structured database for collecting information associated with unsafe human actions that could be used to support quantification (e, g., Hallbert et al [67]). …”

Section: Can We Predict Unsafe Human Actions and Their Likelihoods?mentioning

confidence: 99%

An overview of the evolution of human reliability analysis in the context of probabilistic risk assessment.

Bley

Lois

Kolaczkowski

et al. 2009

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Since the Reactor Safety Study in the early 1970's, human reliability analysis (HRA) has been evolving towards a better ability to account for the factors and conditions that can lead humans to take unsafe actions and thereby provide better estimates of the likelihood of human error for probabilistic risk assessments (PRAs). The purpose of this paper is to provide an overview of recent reviews of operational events and advances in the behavioral sciences that have impacted the evolution of HRA methods and contributed to improvements. The paper discusses the importance of human errors in complex human-technical systems, examines why humans contribute to accidents and unsafe conditions, and discusses how lessons learned over the years have changed the perspective and approach for modeling human behavior in PRAs of complicated domains such as nuclear power plants. It is argued that it has become increasingly more important to understand and model the more cognitive aspects of human performance and to address the broader range of factors that have been shown to influence human performance in complex domains. The paper concludes by addressing the current ability of HRA to adequately predict human failure events and their likelihood.4

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“…These data can be obtained from operational practice and event reports, cognitive simulations, such as the Man‐Machine Integration Design and Analysis System (MIDAS) and the Information, Decision, Action in Crew context (IDAC) model, and expert judgment . Researchers working on HRA have also been focusing increasingly on analyzing and refining empirical data from simulator studies . Simulators can be roughly classified into two types: microworld simulators and full‐scope simulators .…”

Section: Introductionmentioning

confidence: 99%

“…Second, the BN cannot address another type of interrelationships, that is, the effect of one PSF on HEP is dependent on the status of other PSFs. Microworld studies have the possibility to empirically investigate the interrelationships between PSFs in controlled experiments . In addition, the data collected from the experiments might improve the accuracy of the BN …”

Section: Introductionmentioning

confidence: 99%

Human Error Data Collection and Comparison with Predictions by SPAR‐H

Liu

2014

Risk Analysis

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There is a scarcity of empirical data on human error for human reliability analysis (HRA). This situation can increase the variability and impair the validity of HRA outcomes in risk analysis. In this work, a microworld study was used to investigate the effects of performance shaping factors (PSFs) and their interrelationships and combined effects on the human error probability (HEP). The PSFs involved were task complexity, time availability, experience, and time pressure. The empirical data obtained were compared with predictions by the Standardized Plant Analysis Risk-Human Reliability Method (SPAR-H) and data from other sources. The comparison included three aspects: (1) HEP, (2) relative effects of the PSFs, and (3) error types. Results showed that the HEP decreased with experience and time availability levels. The significant relationship between task complexity and the HEP depended on time availability and experience, and time availability affected the HEP through time pressure. The empirical HEPs were higher than the HEPs predicted by SPAR-H under different PSF combinations, showing the tendency of SPAR-H to produce relatively optimistic results in our study. The relative effects of two PSFs (i.e., experience/training and stress/stressors) in SPAR-H agreed to some extent with those in our study. Several error types agreed well with those from operational experience and a database for nuclear power plants (NPPs).

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The use of empirical data sources in HRA

Cited by 27 publications

References 3 publications

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

An overview of the evolution of human reliability analysis in the context of probabilistic risk assessment.

Human Error Data Collection and Comparison with Predictions by SPAR‐H

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