| Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex
systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA
methods currently used by regulatory institutes or utilities are called static HRA and are carried
out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic
challenges in static HRA. For example, existing static HRA does not realistically model and
evaluate human actions as they would be performed at actual systems. There is no method with
HRA to objectively estimate the time required for human actions despite being essential to HRA
processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s,
from unrelated industry experience or simply from expert judgment. Accordingly, this study
attempted to research how to overcome the challenges of existing HRA via dynamic risk
assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this
study developed a dynamic HRA method, named as PRocedure-based Investigation Method of
EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly
concentrates on providing HRA analysts with specific guidelines on how to reasonably model
human actions, assign human reliability data and evaluate output of simulation within a dynamic
probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked
Diagrams (EMRALD). Second, this study also developed a module for performance shaping
factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then
implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed
an HRA data collection framework to support dynamic HRA, called as Simplified Human Error
Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA
or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified
simulators to complement—but not replace—data collection studies using full-scope simulators
and actual operators. To date, many experiments have been conducted under the SHEEP
framework. Multiple analyses, such as human performance analysis, human error analysis, task
complexity analysis, learning effect analysis and time distribution analysis, were also carried out
using the collected data. Then, based on the major insights, an approach to inferring full-scope
data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research
are expected to evaluate human actions more realistically than existing static HRA, provide an
opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance
the quality of HRA.
Key Words: Nuclear Power Plant, Probabilistic Risk Assessment, Human Reliability Analysis,
Dynamic Risk Assessment, Data Collection, Human Performance, Simulator Research |