Structural reliability assessment based on low-discrepancy adaptive importance sampling and artificial neural network

In the field of structural reliability, the estimation of failure probability often requires large numbers of time-consuming performance function calls. It is a great challenge to keep the number of function calls to a minimum extent. The aim of this paper is to propose an approach to assess the structural reliability in an efficient way. The proposed method could be viewed as a hybrid reliability method which combines the advantages of adaptive importance sampling, low-discrepancy sampling and artificial neural network. In the proposed method, artificial neural network is introduced to alleviate the computational burden of deterministic and boring engineering analysis, and its introduction guarantees the computational efficiency of the proposed method. While the Markov chain process is adopted to generate the experimental samples which are used to construct the artificial neural network, the introduction of Markov chain process guarantees the adaptivity of the proposed method and makes the proposed method applicable for various reliability problems. The proposed method is shown to be very efficient as the estimated failure probability is very accurate and only a small number of calls to the actual performance function are needed. The effectiveness and engineering applicability of the proposed method are demonstrated by several test examples.

[1]  Manolis Papadrakakis,et al.  Accelerated subset simulation with neural networks for reliability analysis , 2012 .

[2]  Qiusheng Li,et al.  A new artificial neural network-based response surface method for structural reliability analysis , 2008 .

[3]  J. Halton.  On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals , 1960 .

[4]  Fred J. Hickernell,et al.  Randomized Halton sequences , 2000 .

[5]  L. Schueremans,et al.  Benefit of splines and neural networks in simulation based structural reliability analysis , 2005 .

[6]  Yongchang Pu,et al.  Reliability analysis of structures using neural network method , 2006 .

[7]  Zhenzhou Lu,et al.  A new method on ANN for variance based importance measure analysis of correlated input variables , 2012 .

[8]  Zhenzhou Lu,et al.  A novel adaptive importance sampling algorithm based on Markov chain and low-discrepancy sequence , 2013 .

[9]  Bak Kong Low,et al.  Probabilistic analysis of laterally loaded piles using response surface and neural network approaches , 2012 .

[10]  I. Sobol,et al.  On quasi-Monte Carlo integrations , 1998 .

[11]  Jian Deng,et al.  Structural reliability analysis for implicit performance function using radial basis function network , 2006 .

[12]  Irfan Kaymaz,et al.  Application Of Kriging Method To Structural Reliability Problems , 2005 .

[13]  Costas Papadimitriou,et al.  Reliability of uncertain dynamical systems with multiple design points , 1999 .

[14]  Wei Wang,et al.  Application of low-discrepancy sampling method in structural reliability analysis , 2009 .

[15]  Manolis Papadrakakis,et al.  Reliability-based structural optimization using neural networks and Monte Carlo simulation , 2002 .

[16]  N. Gayton,et al.  CQ2RS: a new statistical approach to the response surface method for reliability analysis , 2003 .

[17]  J. Beck,et al.  A new adaptive importance sampling scheme for reliability calculations , 1999 .

[18]  Wei Wang,et al.  Structural Reliability Assessment by Local Approximation of Limit State Functions Using Adaptive Markov Chain Simulation and Support Vector Regression , 2012, Comput. Aided Civ. Infrastructure Eng..

[19]  S. Rahman,et al.  A univariate approximation at most probable point for higher-order reliability analysis , 2006 .

[20]  H. Gomes,et al.  COMPARISON OF RESPONSE SURFACE AND NEURAL NETWORK WITH OTHER METHODS FOR STRUCTURAL RELIABILITY ANALYSIS , 2004 .

[21]  Christian Bucher,et al.  Importance sampling for first passage problems of nonlinear structures , 1999 .

[22]  Xibing Li,et al.  Structural reliability analysis for implicit performance functions using artificial neural network , 2005 .

[23]  Zhenzhou Lu,et al.  Conditional probability Markov chain simulation based reliability analysis method for nonnormal variables , 2010 .

[24]  Nicolas Gayton,et al.  AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation , 2011 .