Construction of response surfaces based on progressive-lattice-sampling experimental designs with application to uncertainty propagation

Romero, Vicente J.; Swiler, Laura Painton; Giunta, Anthony A.

doi:10.1016/j.strusafe.2003.03.001

Cited by 110 publications

(65 citation statements)

References 10 publications

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“…However, Literature [4] showed that the accuracy of Kriging model approach is not higher than that of RSM if  is inappropriate. In DACE [13], *  is found by the pattern search method, but the result is greatly affected by the given initial value  because pattern search method has to start with the initial  , which can be seen in Section 2.2.2.2.…”

Section: Pso-optimized Krigingmentioning

confidence: 99%

“…Romero [4], the first researcher applied Kriging on structural reliability problems, compared Kriging with polynomial regression and finite-element interpolation by the Latin hypercube sampling method. Kaymaz [5] calculate structural reliability based on Bucher's design and central composite design, and compared Kriging model method with classic response surface method.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling

Tong

Gong

2018

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. This paper aims to reduce the computational cost of reliability analysis. A new hybrid algorithm is proposed based on PSO-optimized Kriging model and adaptive importance sampling method. Firstly, the particle swarm optimization algorithm (PSO) is used to optimize the parameters of Kriging model. A typical function is fitted to validate improvement by comparing results of PSO-optimized Kriging model with those of the original Kriging model. Secondly, a hybrid algorithm for reliability analysis combined optimized Kriging model and adaptive importance sampling is proposed. Two cases from literatures are given to validate the efficiency and correctness. The proposed method is proved to be more efficient due to its application of small number of sample points according to comparison results. IntroductionIn recent years, the first-order reliability method (FORM) or the second-order reliability method (SORM) 5-6 is often applied to calculating the reliability, but the accuracy of the estimated reliability is very low because FORM and SORM are based on Taylor expansion, which ignores higher order terms. It is well known that the Monte Carlo simulation (MCS) is a widely accepted and robust method, but it is impossible to calculate the reliability with a great many samples in a short time. Especially, in case that the performance function is a complicated and time-consuming process.To reduce computations of the expensive performance function, the Kriging surrogate model been used to deal with this issue for many years. Romero [4], the first researcher applied Kriging on structural reliability problems, compared Kriging with polynomial regression and finite-element interpolation by the Latin hypercube sampling method. Kaymaz [5] calculate structural reliability based on Bucher's design and central composite design, and compared Kriging model method with classic response surface method. Choi [6] proposed an Bayesian reliability algorithm based on the Kriging dimension reduction method. Echard [7] proposed an active learning function for updating the design of experiment (DoE) base on Kriging, and compared it with other metamodels [8]. Dubourg [9] developed a hybrid algorithm based on Kriging model to solve reliability-based design optimization (RBDO). Zhang [10] used Kriging model for geotechnical reliability ansys. Balesdent [11] developed an adaptive importance sampling method based on Kriging when event probability estimation is rare. Tong [12] proposed a hybrid adaptive method by combining subset simulation and Kriging. In recent years, most studies concentrate more on how to apply Kriging model develped by Lophaven [13] to the structural reliability. However, study finds the solution of Kriging model developed by Lophaven is greatly affected by the given initial value  , and the pattern search method by Lophaven may lead

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Section: Pso-optimized Krigingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling

Tong

Gong

2018

IOP Conf. Ser.: Earth Environ. Sci.

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“…There are several types of metamodel, including the Response Surface Methodology (RSM) and Kriging are cited. The application of the kriging technique for assessing the reliability of structures is very recent [8,22]. Several studies [3,14,10,21,23] have shown growing interest in this type of metamodel for assessing the probability of failure.…”

Section: Introductionmentioning

confidence: 99%

Adaptive response surface by kriging using pilot points for structural reliability analysis

Nassim¹

2013

IOSR-JMCE

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“…In the context of Finite Element (FE) modeling, the Stochastic Finite Element Modeling (SFEM) based on perturbation, spectral Neumann expansion, projection on a polynomial chaos or direct Monte-Carlo simulation methods are the most popular approaches widely developed these last few years [1][2][3][4][5]. Methods based on non-determinist arithmetic operations (fuzzy [6][7], interval [8]) or on response surface [9][10][11][12] are also interesting alternatives to SFEM. For a detailed review, the reader could refer to [13] which present a very interesting state-of-the-art on computational stochastic mechanics.…”

Section: Introductionmentioning

confidence: 99%

Efficient structural computations with parameters uncertainty for composite applications

Carrère¹,

Rollet²,

Leroy³

et al. 2009

Composites Science and Technology

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Construction of response surfaces based on progressive-lattice-sampling experimental designs with application to uncertainty propagation

Cited by 110 publications

References 10 publications

A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling

A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling

Adaptive response surface by kriging using pilot points for structural reliability analysis

Efficient structural computations with parameters uncertainty for composite applications

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