How to effectively compute the reliability of a thermal–hydraulic nuclear passive system

Zio, Enrico; Pedroni, Nicola

doi:10.1016/j.nucengdes.2010.10.029

Cited by 34 publications

(11 citation statements)

References 105 publications

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“…The estimates given by the bootstrapped ANNs, ̅ ! ( ), are in general more accurate than the estimate of the best ANN, JFF ( ), in the bootstrap ensemble of ANNs (that is the one trained with the original data set, Dtrain, as shown in step 1) above) [Zio, 2006;Zio and Pedroni, 2011]. Actually, the ANNs ensemble has diverse (higher) generalization capabilities than the best ANN.…”

Section: Bootstrap Approach For Ann Uncertainty Estimationmentioning

confidence: 99%

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Bootstrapped Artificial Neural Networks for the seismic analysis of structural systems

et al. 2017

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We look at the behavior of structural systems under the occurrence of seismic events with the aim of identifying the fragility curves. Artificial Neural Network (ANN) empirical regression models are employed as fast-running surrogates of the (long-running) Finite Element Models (FEMs) that are typically adopted for the simulation of the system structural response. However, the use of regression models in safety critical applications raises concerns with regards to accuracy and precision. For this reason, we use the bootstrap method to quantify the uncertainty introduced by the ANN metamodel. An application is provided with respect to the evaluation of the structural damage (in this case, the maximal top displacement) of a masonry building subject to seismic risk. A family of structure fragility curves is identified, that accounts for both the (epistemic) uncertainty due to the use of ANN metamodels and the (epistemic) uncertainty due to the paucity of data available to infer the fragility parameters.

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Section: Bootstrap Approach For Ann Uncertainty Estimationmentioning

confidence: 99%

“…The effectiveness of ANN and bootstrap methods in robustly quantifying the uncertainty associated with (safety-related) estimates (possibly obtained with small-sized data sets) has been thoroughly demonstrated in the open literature: see, e.g., [Efron and Thibshirani, 1993;Zio, 2006;Pedroni et al, 2010;Zio and Pedroni, 2011].…”

Section: Introductionmentioning

confidence: 99%

Bootstrapped Artificial Neural Networks for the seismic analysis of structural systems

et al. 2017

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“…6 and 8. This can be improved by resorting to more efficient MC techniques able to dealing with low probability estimation (Zio and Pedroni, 2011).…”

Section: Monte Carlo Estimation Of S-and D-dependent Transition Ratesmentioning

confidence: 99%

A Multi-State Physics Modeling approach for the reliability assessment of Nuclear Power Plants piping systems

Maio

Colli

Zio

et al. 2015

Annals of Nuclear Energy

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“…Notice that the recommendation of using ANN regression models is mainly based on theoretical considerations about the (mathematically) demonstrated capability of ANN regression models of being universal approximants of continuous nonlinear functions [21] and the experience of the authors' in the use of ANN regression models for propagating the uncertainties through mathematical model codes simulating safety systems [56][57][58][59][60]. Since no further comparisons with other types of regression models have been performed by the authors yet, no additional proofs of the superiority of ANNs with respect to other regression models can be provided at present, in general terms.…”

Section: Sensitivity Analysis In a Factor Prioritization Settingmentioning

confidence: 99%

Hybrid Uncertainty and Sensitivity Analysis of the Model of a Twin-Jet Aircraft

Pedroni

Zio

2015

Journal of Aerospace Information Systems

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International audienceThe mathematical models employed in the risk assessment of complex, safety-critical engineering systems cannot capture all the characteristics of the system under analysis, due to: (i) the intrinsically random nature of several of the phenomena occurring during system operation (aleatory uncertainty); (ii) the incomplete knowledge about some of the phenomena (epistemic uncertainty). In this work, we consider the model of a twin-jet aircraft, which includes twenty-one inputs and eight outputs. The inputs are affected by mixed aleatory and epistemic uncertainties represented by probability distributions and intervals, respectively. Within this context, we address the following issues: (A) improvement of the input uncertainty models (i.e., reduction of the corresponding epistemic uncertainties) based on experimental data; (B) sensitivity analysis to rank the importance of the inputs in contributing to output uncertainties; (C) propagation of the input uncertainties to the outputs; (D) extreme case analysis to identify those system configurations that prescribe extreme values of some system performance metrics of interest (e.g., the failure probability). All the tasks are tackled and solved by means of an efficient combination of: (i) Monte Carlo Simulation (MCS) to propagate the aleatory uncertainty described by probability distributions; (ii) Genetic Algorithms (GAs) to solve the numerous optimization problems related to the propagation of epistemic uncertainty by interval analysis, and (iii) fast-running Artificial Neural Network (ANN) regression models to reduce the computational time related to the repeated model evaluations required by uncertainty and sensitivity analyses

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How to effectively compute the reliability of a thermal–hydraulic nuclear passive system

Abstract: To cite this version:Enrico Zio, Nicola Pedroni. How to effectively compute the reliability of a thermal-hydraulic nuclear passive system. Nuclear Engineering and Design, Elsevier, 2011, 241 (1)

Cited by 34 publications

References 105 publications

Bootstrapped Artificial Neural Networks for the seismic analysis of structural systems

Bootstrapped Artificial Neural Networks for the seismic analysis of structural systems

A Multi-State Physics Modeling approach for the reliability assessment of Nuclear Power Plants piping systems

Hybrid Uncertainty and Sensitivity Analysis of the Model of a Twin-Jet Aircraft

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