The use of the SACADA taxonomy to analyze simulation records: Insights and suggestions

• Published in: Reliability engineering & system safety 2017-03, Vol.159, p.174-183
• Main Authors: Park, J., Chang, Y.J., Kim, Y., Choi, S., Kim, S., Jung, W.
• Format: Article
• Language: English
• Subjects: Authoring; Cognitive ability; Computer simulation; Data collection; Data collection framework; Data processing; Electric power plants; Human error; Human performance; Human reliability analysis; Identification methods; Literature reviews; Nuclear electric power generation; Nuclear energy; Nuclear engineering; Nuclear power plant; Nuclear power plants; Nuclear safety; Operator performance; Reliability; Reliability analysis; Reliability engineering; SACADA taxonomy; Simulator experiment; Simulators; System effectiveness; Taxonomy
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2016.11.002

It is evident that diverse human reliability analysis (HRA) methods are effective for enhancing the safety of socio-technical systems through identifying the most vulnerable tasks to human errors with the associated human error probabilities. This means that reliable human performance data is an important factor affecting HRA quality. Therefore, many researchers have developed technical underpinnings (such as guidelines and taxonomies) that specify what and how HRA data can be collected from simulator experiments. Here, SACADA (Scenario Authoring, Characterization, and Debriefing Application) taxonomy recently developed by US NRC (Nuclear Regulatory Commission) is worth emphasizing, because it is constructed on the basis of a cognitive model (i.e., a top-down approach) while most of the technical underpinnings are developed by a bottom-up approach (i.e., the intensive review of existing literature). For this reason, in this study, the SACADA taxonomy is used to analyze several audio-visual records collected from the full scope simulators of nuclear power plants in the Republic of Korea. The results indicate that the SACADA taxonomy is useful to collect operator performance data in simulator training for HRA. Certain human performance information that can be provided by SACADA data provided are difficult to be covered by the bottom-up approach. •HRA is important for enhancing the safety of socio-technical systems.•HRA quality is largely dependent on the availability of HRA data.•One of the HRA data sources is to use a full-scope simulator.•Data contents to be collected from the full-scope simulator should be clarified.•SACADA is helpful for clarifying data contents from the cognitive perspective.