An improved radial basis function network for structural reliability analysis

Dai, Hongzhe; Zhao, Wei; Wang, W.; Cao, Zhenggang

doi:10.1007/s12206-011-0704-5

Cited by 33 publications

(13 citation statements)

References 19 publications

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“…Response surface method (RSM, also called surrogate model) is widely applied in reliability analysis. Fei et al developed decomposed-coordinated surrogate modeling strategy for compound function approximation and a turbine-blisk reliability evaluation [7], and the method was applied aeroengine blade-tip clearance and its components [8][9][10]; Li et al employed support vector machine in structural reliability analysis [11]; Kaymaz applied Kriging method to complete structural reliability problems [12]; Xiong et al presented a double weighted stochastic RSM for reliability analysis [13]; Gavin et al gave the RSM-based high-order limit state functions for reliability analysis [14]; Ren et al established neural network response surface model for reliability analysis based on artificial neural network (ANN) with high accuracy and nonlinear mapping capability [15]; Dai et al applied this ANN-RSM to the regression analysis of complex structure limit state function to improve the accuracy of reliability analysis [16]. e above works mainly focus on structural reliability and prompt plentiful approaches on evaluating, analyzing, and designing structural reliability.…”

Section: Introductionmentioning

confidence: 99%

Transient Reliability Evaluation Approach of Flexible Mechanism with GA-Extremum Neural Network

Zhao

Fei

et al. 2020

Mathematical Problems in Engineering

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Efficient analytical model directly enhances the reliability evaluation of flexible mechanism under operation. In this paper, genetic algorithm-based extremum neural network (GA-ENN) is developed as reliability model by introducing the thoughts of extremum and genetic algorithm (GA) into artificial neural network to address the key problems comprising transient response and modeling precision in the dynamic reliability analysis of flexible mechanism in a time domain. The thought of extremum is adopted to simplify transient response process as one extremum value to the difficulty of dynamic reliability analysis induced by transient process response, and the GA is applied to find the optimal model parameters of reliability model. The dynamic reliability analysis of two-link flexible robot manipulator (TFRM) (a typical flexible mechanism) was implemented based on the GA-ENN method, regarding the input random variables of material density, elastic modulus, section sizes of components, and the output response of components’ deformations. From the analysis, the comprehensive reliability of the TFRM is 0.951 when the allowable deformation is 1.8 × 10−2 m. Besides, the maximum deformations of the two components follow the normal distributions with the means of 1.45 × 10−2 m and 1.69 × 10−2 m and the standard variances of 6.77 × 10−4 m and 4.08 × 10−4 m, respectively. Through the comparison of methods, it is illustrated that the developed GA-ENN improves the simulation efficiency and modeling accuracy by overcoming the problems of transient response and model parameter optimization in the dynamic reliability analysis of TFRM.

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Section: Introductionmentioning

confidence: 99%

Transient Reliability Evaluation Approach of Flexible Mechanism with GA-Extremum Neural Network

Zhao

Fei

et al. 2020

Mathematical Problems in Engineering

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“…Local RSM methods such as the moving least square technique were developed to handle highly nonlinear limit state functions [31]. Other commonly used surrogate modeling methods have also been developed over the years, such as artificial neural networks (ANN) [32][33][34][35][36][37], Kriging [38][39][40][41][42][43][44][45][46], high-dimensional or factorized high-dimensional model representation [47][48][49][50][51], support vector machine [52][53][54][55][56][57], radial basis functions (RBFs) [58], and even ensemble of surrogates [59][60][61][62].…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis Based on Surrogate Modeling Methods

Wang¹

2020

Reliability and Maintenance - An Overview of Cases

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Various surrogate modeling methods have been developed to generate approximate functions of expensive numerical simulations. They can be used in reliability analysis when integrated with a numerical reliability analysis method such as a first-order or second-order reliability analysis method (FORM/SORM), or Monte Carlo simulations (MCS). In this chapter, a few surrogate modeling methods are briefly reviewed. A reliability analysis approach using surrogate models based on radial basis functions (RBFs) and successive RBFs is presented. The RBF surrogate modeling method is a special type of interpolation method, as the model passes through all available sample points. Augmented RBFs are adopted to create approximate models of a limit state/performance function, before the failure probability can be computed using MCS. To improve model efficiency, a successive RBF (SRBF) surrogate modeling method is investigated. Several mathematical and practical engineering examples are solved. The failure probabilities computed using the SRBF surrogate modeling method are fairly accurate, when a reasonable sample size is used to create the surrogate models. The method based on augmented RBF surrogate models is useful for probabilistic analysis of practical problems, such as civil and mechanical engineering applications.

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“…Recently, artificial neural network (ANN) has been proposed as an alternative metamodel for structural reliability analysis. Multilayer perceptrons (MLPs) network and radial basis function network are the most used type of ANN for approximating the limit state (Adeli, ; Beer and Spanos, ; Bolourchi et al., ; Dai et al., ; Hurtado, ; Sun and Betti, ). It has been shown that ANN has practical advantages over the response surface methods because of its superior mapping capacities and the flexibility in functional form (Adeli and Hung, ; Bucher and Most, ; Hurtado and Alvarez, ).…”

Section: Introductionmentioning

confidence: 99%

A Wavelet Support Vector Machine‐Based Neural Network Metamodel for Structural Reliability Assessment

Dai

Cao

2017

Computer aided Civil Eng

111

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Wavelet neural network (WNN) has been widely used in the field of civil engineering. However, WNN can only effectively handle problems of small dimensions as the computational cost for constructing wavelets of large dimensions is prohibitive. To expand the application of WNN to higher dimensions, this article develops a new wavelet support vector machine (SVM)based neural network metamodel for reliability analysis. The method first develops an autocorrelation wavelet kernel SVM and then uses a set of wavelet SVMs with different resolution as the activation function of WNN. The output of network is obtained through aggregating outputs of different wavelet SVMs. The method takes advantage of the excellent capacities of SVM to handle highdimensional problems and of the attractive properties of wavelet to represent complex functions. Four examples are given to demonstrate the application and effectiveness of the proposed method.

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An improved radial basis function network for structural reliability analysis

Cited by 33 publications

References 19 publications

Transient Reliability Evaluation Approach of Flexible Mechanism with GA-Extremum Neural Network

Transient Reliability Evaluation Approach of Flexible Mechanism with GA-Extremum Neural Network

Reliability Analysis Based on Surrogate Modeling Methods

A Wavelet Support Vector Machine‐Based Neural Network Metamodel for Structural Reliability Assessment

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