A Stochastic Dual Response Surface Method for Reliability Analysis Considering the Spatial Variability

To solve spectral stochastic finite element problems, the collocation-based spectral stochastic finite element method (SSFEM) was developed, and the Stochastic Response Surface Method (SRSM) was used to represent uncertainty propagation. Therefore, the accuracy of SRSM is important for obtaining more accurate probabilistic results. The weighted SRSM was developed to improve the global accuracy of SRSM, but it is not suitable for random field problems because weights might distort the response surface. In this study, a new Stochastic Dual-response Surface Method (SDRSM) was developed to improve the accuracy of SRSM. The SDRSM combines the conventional SRSM and target-weighted SRSM (TWSRSM), which is assigned a weight for the numerical result corresponding to the collocation point. Then, the proposed method was extended to deal with problems involving random fields. For comparison with the conventional methods (SRSM and WSRSM), two numerical examples involving random fields were carried out. Compared with Monte Carlo simulation results, SDRSM shows the smallest error without the addition of the collocation points. In addition, the mean absolute errors for equally spaced probability intervals were compared, and their mean and standard deviation of SDRSM were relatively smaller than that of other methods.

[1]  R. Ghanem,et al.  Stochastic Finite Elements: A Spectral Approach , 1990 .

[2]  G. Stefanou The stochastic finite element method: Past, present and future , 2009 .

[3]  Ying Xiong,et al.  Weighted stochastic response surface method considering sample weights , 2011 .

[4]  Goodarz Ahmadi,et al.  Application of Wiener-Hermite Expansion to Nonstationary Random Vibration of a Duffing Oscillator , 1983 .

[5]  Mircea Grigoriu,et al.  STOCHASTIC FINITE ELEMENT ANALYSIS OF SIMPLE BEAMS , 1983 .

[6]  G. Pande,et al.  Stochastic Finite Element Analysis using Polynomial Chaos , 2016 .

[7]  Haym Benaroya,et al.  PARAMETRIC RANDOM EXCITATION. I: EXPONENTIALLY CORRELATED PARAMETERS. , 1987 .

[8]  George Adomian,et al.  Solving Frontier Problems of Physics: The Decomposition Method , 1993 .

[9]  S. Mahadevan,et al.  Collocation-based stochastic finite element analysis for random field problems , 2007 .

[10]  S. Isukapalli,et al.  Stochastic Response Surface Methods (SRSMs) for Uncertainty Propagation: Application to Environmental and Biological Systems , 1998, Risk analysis : an official publication of the Society for Risk Analysis.

[11]  Pol D. Spanos,et al.  Karhunen-Loéve Expansion of Stochastic Processes with a Modified Exponential Covariance Kernel , 2007 .

[12]  Ted Belytschko,et al.  A coupled finite element-element-free Galerkin method , 1995 .

[13]  Menner A Tatang,et al.  Direct incorporation of uncertainty in chemical and environmental engineering systems , 1995 .

[14]  Marcin Marek Kaminski,et al.  The Stochastic Perturbation Method for Computational Mechanics , 2013 .

[15]  R. Ghanem,et al.  Stochastic Finite Element Expansion for Random Media , 1989 .

[16]  Wei Gao,et al.  Stochastic finite element analysis of structures in the presence of multiple imprecise random field parameters , 2016 .

[17]  George Stefanou,et al.  Assessment of spectral representation and Karhunen–Loève expansion methods for the simulation of Gaussian stochastic fields , 2007 .

[18]  S. Adhikari,et al.  A reduced polynomial chaos expansion method for the stochastic finite element analysis , 2012 .

[19]  Mircea Grigoriu,et al.  Evaluation of Karhunen–Loève, Spectral, and Sampling Representations for Stochastic Processes , 2006 .

[21]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[22]  Bruno Sudret,et al.  Adaptive sparse polynomial chaos expansion based on least angle regression , 2011, J. Comput. Phys..

[23]  Lee Margetts,et al.  Practical Application of the Stochastic Finite Element Method , 2016 .

[24]  J. Villadsen,et al.  Solution of differential equation models by polynomial approximation , 1978 .

[25]  Marcin Kamiński,et al.  The Stochastic Perturbation Method for Computational Mechanics: Kamiński/The Stochastic Perturbation Method for Computational Mechanics , 2013 .