Quantitative functional failure analysis of a thermal–hydraulic passive system by means of bootstrapped Artificial Neural Networks

Zio, Enrico; Apostolakis, G.; Pedroni, Nicola

doi:10.1016/j.anucene.2010.02.012

Cited by 33 publications

(13 citation statements)

References 51 publications

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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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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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“…For example, in linear regression, Cribari-Neto [24] and Cribari-Neto and Lima [25] use bootstrapped hypothesis testing and intervals tailored to account for heteroskedasticity with an estimator of the covariance matrix that considers the effects of leverage points in the design matrix. In nonparametric regression, Zio [26], Cadini et al [27], Secchi et al [28] and Zio et al [29] analyze by bootstrap the uncertainty of ANN predictions of nuclear process parameters. In the context of support vector machines, bootstrap approaches have been mainly applied to classification problems [30,31].…”

Section: Introductionmentioning

confidence: 99%

Computing confidence and prediction intervals of industrial equipment degradation by bootstrapped support vector regression

Lins

Droguett

Moura

et al. 2015

Reliability Engineering & System Safety

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“…On one side, fast-running surrogate regression models (also called response surfaces or metamodels) can be properly built to mimic the response of the original T-H model code: since the metamodel is, in general, much faster to be evaluated, the problem of long computing times is circumvented. Several kinds of surrogate metamodels have been recently applied to safety-related nuclear, structural and hydrogeological problems, including polynomial Response Surfaces (RSs) [40][41][42][43] , polynomial chaos expansions [44][45][46][47] , stochastic collocations [4 8,4 9] , Artificial Neural Networks (ANNs) [50][51][52][53] , Support Vector Machines (SVMs) [54,55] and kriging [56][57][58][59][60][61] .…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Metamodel-Based Subset Importance Sampling approach for the assessment of the functional failure probability of a thermal-hydraulic passive system

Pedroni

Zio

2017

Applied Mathematical Modelling

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An Adaptive Metamodel-Based Subset Importance Sampling (AM-SIS) approach, previously developed by the authors, is here employed to assess the (small) functional failure probability of a thermal-hydraulic (T-H) nuclear passive safety system. The approach relies on an iterative Importance Sampling (IS) scheme that efficiently couples the powerful characteristics of Subset Simulation (SS) and fast-running Artificial Neural Networks (ANNs). In particular, SS and ANNs are intelligently employed to build and progressively refine a fully nonparametric estimator of the ideal, zero-variance Importance Sampling Density (ISD), in order to: (i) increase the robustness of the failure probability estimates and (ii) decrease the number of expensive T-H simulations needed (together with the related computational burden). The performance of the approach is thoroughly compared to that of other efficient Monte Carlo (MC) techniques on a case study involving an advanced nuclear reactor cooled by helium.

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Quantitative functional failure analysis of a thermal–hydraulic passive system by means of bootstrapped Artificial Neural Networks

Cited by 33 publications

References 51 publications

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

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

Computing confidence and prediction intervals of industrial equipment degradation by bootstrapped support vector regression

An Adaptive Metamodel-Based Subset Importance Sampling approach for the assessment of the functional failure probability of a thermal-hydraulic passive system

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