Symptom-based context quantification for dynamic accident analysis
| Autor: | Gueorgui Petkov |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Teamwork
Computer science media_common.quotation_subject 05 social sciences Blackout 0211 other engineering and technologies Public Health Environmental and Occupational Health Context (language use) 02 engineering and technology Accident analysis Identification (information) Accident (fallacy) Risk analysis (engineering) Accident investigation 021105 building & construction medicine 0501 psychology and cognitive sciences medicine.symptom Safety Risk Reliability and Quality Safety Research 050107 human factors media_common Human reliability |
| Zdroj: | Safety Science. 121:666-678 |
| ISSN: | 0925-7535 |
| DOI: | 10.1016/j.ssci.2018.02.027 |
| Popis: | Accident analysis requires development and improvement of the concepts, methods and tools for support of human performance, creation of resilient technology and implementation of reliable organization for effective decision-making and management. The decision-maker in any situation is a human and her/his successful or erroneous actions are always a meaningful part of the accident investigation. According to current understanding of accidents, human failure events are more often caused by work interactions and conditions than by people. That is why in human reliability assessment (HRA) these investigations are unavailing without a comprehensive description and evaluation of the context in which the socio-technical systems interact and keep balance. The paper presents the capacities of the Performance Evaluation of Teamwork (PET) method and its procedure for context quantification based on the human, organization and technology symptoms during accidents for monitoring, evaluation and analysis of their progression. It explains how this method assesses accident context potential as probability on the macroscopic socio-technical system level. The intermediate use of symptoms for the quantification of the context, as opposed to direct description and use of the causes, gives better opportunities for the dynamic, systematic identification and quantitative interpretation of these causes in complex systems and high reliable organizations. The context quantification is exemplified by thermo-hydraulic simulation of a severe nuclear accident: a long-term total blackout. |
| Databáze: | OpenAIRE |
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