Comparar métodos

	Análise de Árvore de Eventos Baseada em Risco	Análise de Árvore de Falhas Baseada em Risco
Área	Delineamento experimental	Delineamento experimental
Família	Process / pipeline	Process / pipeline
Ano de origem	1975 (WASH-1400); risk-based integration formalized through 1980s–1990s PRA practice	1961 (FTA origin); risk-based integration formalised 1975–1981
Autor original	Originated in nuclear industry (US Nuclear Regulatory Commission, WASH-1400 report); risk-based framing developed through probabilistic risk assessment practice	H.A. Watson (Bell Labs) and developed further by Boeing/U.S. Air Force; risk-based extension via NRC probabilistic risk assessment programs
Tipo	Risk and reliability analysis technique	Quantitative safety and reliability analysis
Fonte seminal	Bedford, T., & Cooke, R. (2001). Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press. ISBN: 978-0521773201	Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. U.S. Nuclear Regulatory Commission, NUREG-0492.
Outros nomes	Risk-based ETA, probabilistic event tree analysis, consequence-probability event tree, risk-informed ETA	RB-FTA, risk-informed FTA, quantitative fault tree analysis, probabilistic fault tree analysis
Relacionados	4	6
Resumo	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.	Risk-based fault tree analysis (RB-FTA) combines classical fault tree analysis with explicit quantitative risk assessment. Starting from an undesired top event, the analyst decomposes it into contributing causes using AND/OR logic gates, assigns failure probabilities to basic events from reliability databases or historical data, and then propagates those probabilities through the tree to compute top-event likelihood. The result is expressed as risk — probability weighted by consequence severity — enabling prioritisation of safety interventions by their actual risk reduction impact.