| Records |
Author  |
Dang, C.; Wei, P.F.; Faes, M.G.R.; Valdebenito, M.A.; Beer, M. |
| Title |
Parallel adaptive Bayesian quadrature for rare event estimation |
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| Year |
2022 |
Publication |
Reliability Engineering & System Safety |
Abbreviated Journal |
Reliab. Eng. Syst. Saf. |
| Volume |
225 |
Issue |
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Pages |
108621 |
| Keywords |
Reliability analysis; Gaussian process; Numerical uncertainty; Bayesian quadrature; Parallel computing |
| Abstract |
Various numerical methods have been extensively studied and used for reliability analysis over the past several decades. However, how to understand the effect of numerical uncertainty (i.e., numerical error due to the discretization of the performance function) on the failure probability is still a challenging issue. The active learning probabilistic integration (ALPI) method offers a principled approach to quantify, propagate and reduce the numerical uncertainty via computation within a Bayesian framework, which has not been fully investigated in context of probabilistic reliability analysis. In this study, a novel method termed `Parallel Adaptive Bayesian Quadrature' (PABQ) is proposed on the theoretical basis of ALPI, and is aimed at broadening its scope of application. First, the Monte Carlo method used in ALPI is replaced with an importance ball sampling technique so as to reduce the sample size that is needed for rare failure event estimation. Second, a multi-point selection criterion is proposed to enable parallel distributed processing. Four numerical examples are studied to demonstrate the effectiveness and efficiency of the proposed method. It is shown that PABQ can effectively assess small failure probabilities (e.g., as low as 10(-7)) with a minimum number of iterations by taking advantage of parallel computing. |
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0951-8320 |
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WOS:000809316300008 |
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UAI @ alexi.delcanto @ |
Serial |
1607 |
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| Author |
Leiva, V.; Ruggeri, F.; Saulo, H.; Vivanco, J.F. |
| Title |
A methodology based on the Birnbaum-Saunders distribution for reliability analysis applied to nano-materials |
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| Year |
2017 |
Publication |
Reliability Engineering & System Safety |
Abbreviated Journal |
Reliab. Eng. Syst. Saf. |
| Volume |
157 |
Issue |
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Pages |
192-201 |
| Keywords |
Bayesian analysis; Hardness data; Markov chain Monte Carlo method; R software |
| Abstract |
The Birnbaum-Saunders distribution has been widely studied and applied to reliability studies. This paper proposes a novel use of this distribution to analyze the effect on hardness, a material mechanical property, when incorporating nano-particles inside a polymeric bone cement. A plain variety and two modified types of mesoporous silica nano-particles are considered. In biomaterials, one can study the effect of nano-particles on mechanical response reliability. Experimental data collected by the authors from a micro-indentation test about hardness of a commercially available polymeric bone cement are analyzed. Hardness is modeled with the Birnbaum-Saunders distribution and Bayesian inference is performed to derive a methodology, which allows us to evaluate the effect of using nano-particles at different loadings by the R software. (C) 2016 Elsevier Ltd. All rights reserved. |
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[Leiva, Victor; Vivanco, Juan F.] Univ Adolfo Ibanez, Fac Sci & Engn, Vina del Mar, Chile, Email: victorleivictorleivasanchez@gmail.com |
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Elsevier Sci Ltd |
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English |
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0951-8320 |
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WOS:000387195700017 |
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UAI @ eduardo.moreno @ |
Serial |
676 |
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Valdebenito, M.A.; Wei, P.F.; Song, J.W.; Beer, M.; Broggi, M. |
| Title |
Failure probability estimation of a class of series systems by multidomain Line Sampling |
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| Year |
2021 |
Publication |
Reliability Engineering & System Safety |
Abbreviated Journal |
Reliab. Eng. Syst. Saf. |
| Volume |
213 |
Issue |
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Pages |
107673 |
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Line sampling; Multidomain; Linear performance function; Failure probability; Series system |
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This contribution proposes an approach for the assessment of the failure probability associated with a particular class of series systems. The type of systems considered involves components whose response is linear with respect to a number of Gaussian random variables. Component failure occurs whenever this response exceeds prescribed deterministic thresholds. We propose multidomain Line Sampling as an extension of the classical Line Sampling to work with a large number of components at once. By taking advantage of the linearity of the performance functions involved, multidomain Line Sampling explores the interactions that occur between failure domains associated with individual components in order to produce an estimate of the failure probability. The performance and effectiveness of multidomain Line Sampling is illustrated by means of two test problems and an application example, indicating that this technique is amenable for treating problems comprising both a large number of random variables and a large number of components. |
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0951-8320 |
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WOS:000663910500016 |
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UAI @ alexi.delcanto @ |
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1430 |
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Yuan, X.K.; Faes, M.G.R.; Liu, S.L.; Valdebenito, M.A.; Beer, M. |
| Title |
Efficient imprecise reliability analysis using the Augmented Space Integral |
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| Year |
2021 |
Publication |
Reliability Engineering & System Safety |
Abbreviated Journal |
Reliab. Eng. Syst. Saf. |
| Volume |
210 |
Issue |
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Pages |
107477 |
| Keywords |
Imprecise reliability analysis; Simulation-based method; Interval variable; Augmented space |
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This paper presents an efficient approach to compute the bounds on the reliability of a structure subjected to uncertain parameters described by means of imprecise probabilities. These imprecise probabilities arise from epistemic uncertainty in the definition of the hyper-parameters of a set of random variables that describe aleatory uncertainty in some of the structure's properties. Typically, such calculation involves the solution of a so-called double-loop problem, where a crisp reliability problem is repeatedly solved to determine which realization of the epistemic uncertainties yields the worst or best case with respect to structural safety. The approach in this paper aims at decoupling this double loop by virtue of the Augmented Space Integral. The core idea of the method is to infer a functional relationship between the epistemically uncertain hyper-parameters and the probability of failure. Then, this functional relationship can be used to determine the best and worst case behavior with respect to the probability of failure. Three case studies are included to illustrate the effectiveness and efficiency of the developed methods. |
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0951-8320 |
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WOS:000663909400008 |
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UAI @ alexi.delcanto @ |
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1432 |
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| Author |
Zhou, C.C.; Zhang, H.L.; Valdebenito, M.A.; Zhao, H.D. |
| Title |
A general hierarchical ensemble-learning framework for structural reliability analysis |
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| Year |
2022 |
Publication |
Reliability Engineering & System Safety |
Abbreviated Journal |
Reliab. Eng. Syst. Saf. |
| Volume |
225 |
Issue |
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Pages |
108605 |
| Keywords |
Reliability analysis; Ensemble-learning; Surrogate model; Performance function; Limit state surface |
| Abstract |
Existing ensemble-learning methods for reliability analysis are usually developed by combining ensemble learning with a learning function. A commonly used strategy is to construct the initial training set and the test set in advance. The training set is used to train the initial ensemble model, while the test set is adopted to allocate weight factors and check the convergence criterion. Reliability analysis focuses more on the local prediction accuracy near the limit state surface than the global prediction accuracy in the entire space. However, samples in the initial training set and the test set are generally randomly generated, which will result in the learning function failing to find the real ???best??? update samples and the allocation of weight factors may be suboptimal or even unreasonable. These two points have a detrimental impact on the overall performance of the ensemble model. Thus, we propose a general hierarchical ensemble-learning framework (ELF) for reliability analysis, which consists of two-layer models and three different phases. A novel method called CESM-ELF is proposed by embedding the classical ensemble of surrogate models (CESM) in the proposed ELF. Four examples are investigated to show that CESM-ELF outperforms CESM in prediction accuracy and is more efficient in some cases. |
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0951-8320 |
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WOS:000809659300003 |
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UAI @ alexi.delcanto @ |
Serial |
1597 |
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