Error-based stopping criterion for the combined adaptive Kriging and importance sampling method for reliability analysis

[1]  Chong Wang,et al.  Evidence theory-based reliability optimization design using polynomial chaos expansion , 2018, Computer Methods in Applied Mechanics and Engineering.

[2]  B. Sudret,et al.  Metamodel-based importance sampling for structural reliability analysis , 2011, 1105.0562.

[3]  Pingfeng Wang,et al.  A Maximum Confidence Enhancement Based Sequential Sampling Scheme for Simulation-Based Design , 2013, DAC 2013.

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

[5]  Zhenzhou Lu,et al.  An efficient reliability analysis method combining adaptive Kriging and modified importance sampling for small failure probability , 2018 .

[6]  Jerome Sacks,et al.  Designs for Computer Experiments , 1989 .

[7]  Hongzhe Dai,et al.  A multiwavelet support vector regression method for efficient reliability assessment , 2015, Reliab. Eng. Syst. Saf..

[8]  Zhenzhou Lu,et al.  A novel step-wise AK-MCS method for efficient estimation of fuzzy failure probability under probability inputs and fuzzy state assumption , 2019, Engineering Structures.

[9]  Ikjin Lee,et al.  Reliability analysis and reliability-based design optimization of roadway horizontal curves using a first-order reliability method , 2015 .

[10]  Yan Shi,et al.  An adaptive multiple-Kriging-surrogate method for time-dependent reliability analysis , 2019, Applied Mathematical Modelling.

[11]  Nicolas Gayton,et al.  AK-SYS: An adaptation of the AK-MCS method for system reliability , 2014, Reliab. Eng. Syst. Saf..

[12]  M. Eldred,et al.  Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions , 2008 .

[13]  Zhenzhou Lu,et al.  AK-SYSi: an improved adaptive Kriging model for system reliability analysis with multiple failure modes by a refined U learning function , 2018, Structural and Multidisciplinary Optimization.

[14]  Jin Cheng,et al.  Reliability analysis of structures using artificial neural network based genetic algorithms , 2008 .

[15]  Wenping Gong,et al.  Subdomain sampling methods - efficient algorithm for estimating failure probability , 2017 .

[16]  Dimos C. Charmpis,et al.  Application of line sampling simulation method to reliability benchmark problems , 2007 .

[17]  M. Pandey,et al.  Structural reliability analysis based on the concepts of entropy, fractional moment and dimensional reduction method , 2013 .

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

[19]  Yan-Gang Zhao,et al.  Moment methods for structural reliability , 2001 .

[20]  Enrico Zio,et al.  An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability , 2014, Reliab. Eng. Syst. Saf..

[21]  Zeyu Wang,et al.  ESC: an efficient error-based stopping criterion for kriging-based reliability analysis methods , 2018, Structural and Multidisciplinary Optimization.

[22]  J. Beck,et al.  Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation , 2001 .

[23]  Nicola Pedroni,et al.  An Adaptive Metamodel-Based Subset Importance Sampling approach for the assessment of the functional failure probability of a thermal-hydraulic passive system , 2017 .

[24]  Zhenzhou Lu,et al.  An efficient method for moment-independent global sensitivity analysis by dimensional reduction technique and principle of maximum entropy , 2019, Reliab. Eng. Syst. Saf..

[25]  Iason Papaioannou,et al.  MCMC algorithms for Subset Simulation , 2015 .

[26]  Atin Roy,et al.  Support vector regression based metamodeling for seismic reliability analysis of structures , 2018, Applied Mathematical Modelling.

[27]  A. M. Hasofer,et al.  Exact and Invariant Second-Moment Code Format , 1974 .

[28]  F. Grooteman Adaptive radial-based importance sampling method for structural reliability , 2008 .

[29]  Pingfeng Wang,et al.  A double-loop adaptive sampling approach for sensitivity-free dynamic reliability analysis , 2015, Reliab. Eng. Syst. Saf..

[30]  M. D. Stefano,et al.  Efficient algorithm for second-order reliability analysis , 1991 .

[31]  Gordon A. Fenton,et al.  Reliability analysis with Metamodel Line Sampling , 2016 .

[32]  Nicolas Gayton,et al.  A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models , 2013, Reliab. Eng. Syst. Saf..

[33]  J. Beck,et al.  Important sampling in high dimensions , 2003 .

[34]  Yan-Gang Zhao,et al.  NEW APPROXIMATIONS FOR SORM : PART 1 By , 1999 .

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

[36]  Zhenzhou Lu,et al.  A modified importance sampling method for structural reliability and its global reliability sensitivity analysis , 2018 .

[37]  M. Tichý First-order third-moment reliability method , 1994 .

[38]  Sankaran Mahadevan,et al.  A Single-Loop Kriging Surrogate Modeling for Time-Dependent Reliability Analysis , 2016 .

[39]  Jian Wang,et al.  LIF: A new Kriging based learning function and its application to structural reliability analysis , 2017, Reliab. Eng. Syst. Saf..

[40]  Tianxiao Zhang,et al.  An improved high-moment method for reliability analysis , 2017 .

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

[42]  Alaa Chateauneuf,et al.  Reliability analysis and inspection updating by stochastic response surface of fatigue cracks in mixed mode , 2011 .

[43]  A Henriques,et al.  An innovative adaptive sparse response surface method for structural reliability analysis , 2018, Structural Safety.

[44]  Lei Wang,et al.  REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis , 2019, Reliab. Eng. Syst. Saf..