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A new adaptive response surface method for reliability analysis

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TLDR
In this paper, a response surface is built from an initial Latin Hypercube Sampling (LHS) where the most significant terms are chosen from statistical criteria and cross-validation method.
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This article is published in Probabilistic Engineering Mechanics.The article was published on 2013-04-01 and is currently open access. It has received 143 citations till now. The article focuses on the topics: Latin hypercube sampling & Reliability (statistics).

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Subset Selection in Regression

TL;DR: Chapman and Miller as mentioned in this paper, Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40, 1990) and Section 5.8.
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LIF: A new Kriging based learning function and its application to structural reliability analysis

TL;DR: Results show that LIF and the new method proposed in this research are very efficient when dealing with nonlinear performance function, small probability, complicated limit state and engineering problems with high dimension.
Journal ArticleDOI

Reliability analysis of structures by iterative improved response surface method

TL;DR: In this article, the authors explored the advantage of moving least squares method (MLSM) over LSM to reduce the number of iterations required to obtain the updated centre point of design of experiment (DOE) to construct the final response surface for efficient reliability analysis of structures.
Journal ArticleDOI

A general failure-pursuing sampling framework for surrogate-based reliability analysis

TL;DR: This work proposes a failure-pursuing sampling framework, which is able to adopt various surrogate models or active learning strategies, and takes into account the joint probability density function of random variables, the individual information at candidate points and the improvement of the accuracy of predicted failure probability.
Journal ArticleDOI

Adaptive approaches in metamodel-based reliability analysis: A review

TL;DR: The extensive and comprehensive discussion presented aims to be a first step for the unification of the field of adaptive metamodeling in reliability; so that future implementations do not exclusively follow individual lines of research that progressively become more narrow in scope, but also seek transversal developments in the field.
References
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Journal ArticleDOI

Estimating the Dimension of a Model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Book

An introduction to the bootstrap

TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.

Estimating the dimension of a model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Journal ArticleDOI

Bootstrap Methods: Another Look at the Jackknife

TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.
Related Papers (5)
Frequently Asked Questions (15)
Q1. What have the authors contributed in "A new adaptive response surface method for reliability analysis" ?

The method, proposed in this paper, addresses these points using sparse response surface and a relevant criterion for results accuracy. This method is applied to several examples and results are discussed. 

The final criterion used to validate results is the size of the interval around the estimated probability of failure, calculated with the bootstrap method. 

since criteria depend on SSE and number of terms in the RS, the selection can be performed with respect to SSE in order to build RS of different sizes (with one term, two terms, three terms, etc) and criteria can be calculated on each RS. 

Within the current literature of reliability, analysis based on meta-models such as kriging, neural network or support vector machine is increasing since mechanical models are more and more complicated. 

the criterion used to estimate the prediction error must be robust and, second, it must be minimized in order to find the best RS from sets of potential terms. 

if four penalized criteria are used, stepwise selection needs repeating four times the procedure, whereas only one can be performed with forward selection. 

response surfaces (RS) are still widely used in structural reliability analysis because of their simplicity and effectiveness. 

A bootstrap method is used in order to estimate variations of RS predictions and an interval of the estimed probability of failure is deduced. 

in a lot of engineering problems, the limit state function comes from finite element discretization and is thus very expensive to evaluate. 

the initial size of ED has been arbitrary fixed to Ninit ¼ 3M, where M is the number of input variables, because it enables to potentially include all linear terms in the RS. 

The importance level is epdf ¼ 0:05, criterion on region convergence is eb ¼ 0:01 and criterion which controls RS quality is set to ers ¼ 0:99. 

the probabilistic framework is considered, which means that input parameters are realizations of random variables or random fields. 

As said before, the best RS among the four, will be selected with cross-validation, i.e. the RS with the highest predictive coefficient Q2 will be chosen. 

As in previous example, reference value of the generalized reliability index has been estimated by Blatman and Sudret [7] with FORM followed by Importance Sampling with 500,000 simulations, and is bREF ¼ 3:51. 

The following probability interval is definedPf low ¼PðGðf̂ðXÞÿêlowÞr0ÞPf up ¼PðGðf̂ðXÞþ êupÞr0Þ:8 <:ð14ÞThis interval is the final information to validate the probability of failure estimated with RS.