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Mechanics of the Mind under Uncertainty and Cognitive Illusion Involving Risk Assessment in National Security
Published in Anna M. Doro-on, Handbook of Systems Engineering and Risk Management in Control Systems, Communication, Space Technology, Missile, Security and Defense Operations, 2023
A probabilistic risk assessment (PRA) is applied to identify and evaluate risks affecting safety and health (i.e., having a potential for injury or illness, loss of life, damage, or unexpected loss of equipment, as well as those affecting the ability to reliably meet mission objectives) (NASA 2010, 2011b). A PRA characterized risk in terms of three basic questions: (1) What can go wrong? (2) How likely is it? (3) What are the consequences? (NASA 2010, 2011b). The process of conducting PRA is (NASA 2010, 2011b; GAO 2012): Step 1: Definition of objectivesStep 2: Scenarios developmentStep 3: Quantification and uncertainty analysisStep 4: Interpretation of resultsStep 5: Documentation
Radiation protection in the nuclear industry
Published in Alan Martin, Sam Harbison, Karen Beach, Peter Cole, An Introduction to Radiation Protection, 2018
Alan Martin, Sam Harbison, Karen Beach, Peter Cole
Probabilistic safety analysis, such as probabilistic safety assessment (PSA) or probabilistic risk assessment (PRA), use quantitative risk assessment techniques to identify all the possible fault and accident scenarios on the plant, and evaluate their probabilities and consequences using best estimate data. Such techniques provide an overview of the plant that demonstrates how well balanced the design is, that is that no undue reliance has been placed on any particular design feature. This complements the deterministic safety analysis that deals with faults on a fault-by-fault basis. As explained in Chapter 10, PRA is often used to show that risk targets have been met, but undue reliance should not be placed on the numbers it produces. They are subject to significant uncertainties, particularly with regard to their treatment of common cause failures and human behaviour, and so, although they are very useful in comparative terms, the final risk numbers should always be treated with caution.
Resilient Planning of Modification Projects in High Risk Systems
Published in Eirik Albrechtsen, Denis Besnard, Oil and Gas, Technology and Humans, 2018
Traditional risk assessment methods such as probabilistic risk assessment (PRA) characterize risks according to the magnitude (severity) of the possible adverse consequence(s), and the likelihood (probability) of occurrence of each consequence. As these methods rely on quantifiable issues and do not take account of issues that are not probable and/or have little impact, it is less likely that latent failures resulting from unwanted performance variability in the planning phase will be taken into consideration. These latent failures often combine with other issues and performance variability in the operational phase of the planned process, and eventually emerge as risks and hazardous situations. Major accidents can be the result of the combination of factors and behaviours that individually may have been regarded as quite normal and efficient if the result had been a success (Hollnagel, 2004, Hollnagel et al., 2006, Hollnagel, 2009).
A systems approach to a resilience assessment for agility
Published in Systems Science & Control Engineering, 2022
Traditionally, probabilistic risk assessment (PRA) has been done using Fault Tree Analysis, HazOp or What if analyses (Keller & Modarres, 2005). Alternative methods include dynamic PRA (Mandelli et al., 2020) and Monte Carlo methods (Kelly & Smith, 2009) which have been applied to single industrial endeavours. In an attempt to make further progress in this direction for any SoS, a requisite ‘agility’ metric will be defined to allow predictive bias from decision maker actions. Here, this will be done on a contrived ad-hoc system to demonstrate the generality of the proposed approach. The eventual intent really being validation of the concept to later enable an approach for integrating agility into our larger societal SoS. More specifically, the intent here is to provide a tangible model for evaluating an arbitrary systems’ agility to mitigate and recover from an unexpected or previously unexperienced combination of inputs resulting in any form of catastrophe (public health, economic, supply chain, social disorder, etc).
Development of the Versatile Test Reactor Probabilistic Risk Assessment
Published in Nuclear Science and Engineering, 2022
David Grabaskas, Jason Andrus, Dennis Henneke, Jonathan Li, Matthew Bucknor, Matthew Warner
Probabilistic risk assessment (PRA) is an important tool for systematically and exhaustively evaluating and reducing the risk associated with a facility through the exploration of potential events and their consequences. In support of the risk-informed, performance-based design and authorization approach for the Versatile Test Reactor (VTR), which is being developed under the direction of the U.S. Department of Energy (DOE), a PRA has been completed for the conceptual VTR design. The use of PRA during the reactor design phase permits risk-informed decision-making regarding the evaluation of system design alternatives and establishment of system design requirements. Utilized as part of a risk-informed authorization process, PRA information is used to assist in the categorization of event sequences; the classification of structures, systems, and components (SSCs); and the evaluation of the adequacy of defense-in-depth (DID).
Monte Carlo simulation-based probabilistic health risk assessment of metals in groundwater via ingestion pathway in the mining areas of Singhbhum copper belt, India
Published in International Journal of Environmental Health Research, 2020
Soma Giri, Abhay Kumar Singh, Mukesh Kumar Mahato
Health risk assessments is the calculations of the probability of deleterious effects in the human body which accounts for risk sources, exposure routes and risk receptors (Man et al. 2013). However, uncertainty is associated with risk assessment intrinsically (Li et al. 2006), where the ‘uncertainty’ can be explicated as a lack of knowledge pertaining to the actual value of a variable (Cohen et al. 1996). Owing to the uncertainties, the deterministic risk assessments may under-estimate or over-estimate the risks and, hence, needs to be considered with a different approach (Koupaie and Eskicioglu 2015). Probabilistic risk assessment (PRA) makes an effort towards this and characterizes the uncertainty by assessing the health risks based on the range and statistical distribution of the exposure variables or parameters (Sander et al. 2006). Monte-Carlo Simulation is suggested by USEPA for PRA which considers the probability distributions for each exposure parameter (Schuhmacher et al. 2001) to calculate the frequency in which an event may occur (Nieuwenhuijsen et al. 2006).