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| Симулационно-подпомогнат анализ на дърво на събитията× | Анализ на дървото на събитията, базиран на риска× | |
|---|---|---|
| Област | Планиране на експеримента | Планиране на експеримента |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1970s–1990s (formalized in probabilistic risk assessment practice) | 1975 (WASH-1400); risk-based integration formalized through 1980s–1990s PRA practice |
| Създател≠ | H.A. Watson (Bell Telephone Laboratories, ETA origins ~1961); Monte Carlo integration of ETA developed in nuclear/aerospace PRA community 1970s–1990s | Originated in nuclear industry (US Nuclear Regulatory Commission, WASH-1400 report); risk-based framing developed through probabilistic risk assessment practice |
| Тип≠ | Probabilistic risk and reliability assessment method | Risk and reliability analysis technique |
| Основополагащ източник≠ | Zio, E. (2009). Reliability engineering: Old problems and new challenges. Reliability Engineering and System Safety, 94(2), 125–141. DOI ↗ | Bedford, T., & Cooke, R. (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press. ISBN: 978-0521773201 |
| Други названия | Monte Carlo ETA, stochastic event tree analysis, simulation-enhanced ETA, probabilistic event tree simulation | Risk-based ETA, probabilistic event tree analysis, consequence-probability event tree, risk-informed ETA |
| Свързани≠ | 6 | 4 |
| Резюме≠ | Simulation-assisted event tree analysis (ETA) extends classical event tree analysis by replacing fixed point-estimate branch probabilities with Monte Carlo or discrete-event simulation. This allows analysts to propagate uncertainty through every branch of the tree and obtain full probability distributions over accident sequences and system outcomes, yielding far richer risk insights than deterministic ETA alone. | Risk-based event tree analysis is a forward-looking, inductive risk assessment technique that models the consequences of an initiating event by tracing binary success/failure branches through safety barriers, then weights each outcome path by its probability to produce quantified risk estimates. Widely applied in nuclear, chemical process, aviation, and infrastructure safety engineering, it sits at the heart of probabilistic risk assessment (PRA) and supports both design decisions and regulatory compliance. |
| ScholarGateНабор от данни ↗ |
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