Comparison of Stochastic Response Surface Method and Response Surface Method for Structure Reliability Analysis

Lin, Ke; Qiu, Haobo; Gao, Liang; Sun, Yifei

doi:10.1109/icicta.2009.509

Cited by 3 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…  are selected, together with experimental points in the previous ( 1) k  iterations, which together constitute the experimental point of the k -th weighted least-squares regression analysis; (5). Calculate the weights of (2 1) k n   experimental points in the k iteration; (6).…”

Section: Basic Stepsmentioning

confidence: 99%

“…The Monte Carlo method, as a highly accurate method of reliability analysis, has always been favored by researchers, but it is computationally intensive so that it cannot be used in practical engineering [1][2][3][4]. To overcome the problem of computational efficiency, researchers developed a response surface method that approximates the implicit limit state function by a series of deterministic experiments using polynomial functions to reduce the amount of computation within an acceptable error range and improve the computational complexity [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research of Structural Reliability Analysis Based on Nonlinear Weighted Response Surface Methodology

Xu¹,

Cao²

2018

Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

View full text Add to dashboard Cite

Response surface method(RSM) is one of the effective methods to solve the reliability problem of implicit structure. In this paper, a new weighting method is proposed based on the response surface method to solve the nonlinear problem. The weighted design method is used to determine the design point of the response surface, a certain number of sample points are obtained through iteration, and then the least square method is used to solve the experimental points, thus obtaining the reliability index. The results of calculation examples show that the proposed method can reduce the sampling point range while ensuring the accuracy of calculation, effectively improve the efficiency of the reliability index, and provide a reasonable and effective method for the reliability analysis of the implicit limit state.

show abstract

Section: Basic Stepsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research of Structural Reliability Analysis Based on Nonlinear Weighted Response Surface Methodology

Xu¹,

Cao²

2018

Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

View full text Add to dashboard Cite

show abstract

“…Nonetheless, there still exist several differences between them such as basic principles, computational procedures, the methods of fitting the performance function, and accuracy and efficiency in estimating the probability of failure and statistical moments of system output response. To our best knowledge, only Lin et al (2009) performed a preliminary comparison between the SRSM and RSM, focusing on the comparison of the basic principles and procedures. Furthermore, the capabilities of the SRSM and RSM in fitting the performance function, and estimating the probability of failure as well as statistical moments of system output response are not explored extensively.…”

Section: Introductionmentioning

confidence: 99%

Capabilities of stochastic response surface method and response surface method in reliability analysis

Jiang¹,

Li²,

Zhou³

et al. 2014

Structural Engineering and Mechanics

View full text Add to dashboard Cite

The stochastic response surface method (SRSM) and the response surface method (RSM) are often used for structural reliability analysis, especially for reliability problems with implicit performance functions. This paper aims to compare these two methods in terms of fitting the performance function, accuracy and efficiency in estimating probability of failure as well as statistical moments of system output response. The computational procedures of two response surface methods are briefly introduced first. Then their capabilities are demonstrated and compared in detail through two examples. The results indicate that the probability of failure mainly reflects the accuracy of the response surface function (RSF) fitting the performance function in the vicinity of the design point, while the statistical moments of system output response reflect the accuracy of the RSF fitting the performance function in the entire space. In addition, the performance function can be well fitted by the SRSM with an optimal order polynomial chaos expansion both in the entire physical and in the independent standard normal spaces. However, it can be only well fitted by the RSM in the vicinity of the design point. For reliability problems involving random variables with approximate normal distributions, such as normal, lognormal, and Gumbel Max distributions, both the probability of failure and statistical moments of system output response can be accurately estimated by the SRSM, whereas the RSM can only produce the probability of failure with a reasonable accuracy.

show abstract

Response Surface Analysis (RSA) optimization of temperature-resistant gel foam fabrication and performance evaluation

Li¹,

Xiao

Li³

et al. 2022

Colloids and Surfaces A: Physicochemical and Engineering Aspect

View full text Add to dashboard Cite

Comparison of Stochastic Response Surface Method and Response Surface Method for Structure Reliability Analysis

Cited by 3 publications

References 5 publications

Research of Structural Reliability Analysis Based on Nonlinear Weighted Response Surface Methodology

Research of Structural Reliability Analysis Based on Nonlinear Weighted Response Surface Methodology

Capabilities of stochastic response surface method and response surface method in reliability analysis

Response Surface Analysis (RSA) optimization of temperature-resistant gel foam fabrication and performance evaluation

Contact Info

Product

Resources

About