AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation. An important challenge in structural reliability is to keep to a minimum the number of calls to the numerical models. Engineering problems involve more and more complex computer codes and the evaluation of the probability of failure may require very time-consuming computations. Metamodels are used to reduce these computation times. To assess reliability, the most popular approach remains the numerous variants of response surfaces. Polynomial Chaos [1] and Support Vector Machine [2] are also possibilities and have gained considerations among researchers in the last decades. However, recently, Kriging, originated from geostatistics, have emerged in reliability analysis. Widespread in optimisation, Kriging has just started to appear in uncertainty propagation [3] and reliability and studies. It presents interesting characteristics such as exact interpolation and a local index of uncertainty on the prediction which can be used in active learning methods. The aim of this paper is to propose an iterative approach based on Monte Carlo Simulation and Kriging metamodel to assess the reliability of structures in a more efficient way. The method is called AK-MCS for Active learning reliability method combining Kriging and Monte Carlo Simulation. It is shown to be very efficient as the probability of failure obtained with AK-MCS is very accurate and this, for only a small number of calls to the performance function. Several examples from literature are performed to illustrate the methodology and to prove its efficiency particularly for problems dealing with high non-linearity, non-differentiability, non-convex and non-connex domains of failure and high dimensionality

References in zbMATH (referenced in 15 articles )

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  2. Liu, Xiao-Xiao; Wang, Yuan-Sheng: A new formulation on seismic risk assessment for reinforced concrete structures with both random and bounded uncertainties (2018)
  3. Razaaly, Nassim; Congedo, Pietro Marco: Novel algorithm using active metamodel learning and importance sampling: application to multiple failure regions of low probability (2018)
  4. Bect, Julien; Li, Ling; Vazquez, Emmanuel: Bayesian subset simulation (2017)
  5. Schöbi, Roland; Sudret, Bruno: Uncertainty propagation of p-boxes using sparse polynomial chaos expansions (2017)
  6. Fetz, Thomas; Oberguggenberger, Michael: Imprecise random variables, random sets, and Monte Carlo simulation (2016)
  7. Margheri, Luca; Sagaut, Pierre: A hybrid anchored-ANOVA - POD/Kriging method for uncertainty quantification in unsteady high-fidelity CFD simulations (2016)
  8. Wang, Hongqiao; Lin, Guang; Li, Jinglai: Gaussian process surrogates for failure detection: a Bayesian experimental design approach (2016)
  9. Kersaudy, Pierric; Sudret, Bruno; Varsier, Nadège; Picon, Odile; Wiart, Joe: A new surrogate modeling technique combining Kriging and polynomial chaos expansions - application to uncertainty analysis in computational dosimetry (2015)
  10. Yang, Xufeng; Liu, Yongshou; Zhang, Yishang; Yue, Zhufeng: Hybrid reliability analysis with both random and probability-box variables (2015)
  11. Zhang, Yishang; Liu, Yongshou; Yang, Xufeng: Parametric sensitivity analysis for importance measure on failure probability and its efficient Kriging solution (2015)
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  13. Wang, Pan; Lu, Zhenzhou; Tang, Zhangchun: An application of the Kriging method in global sensitivity analysis with parameter uncertainty (2013)
  14. Hurtado, Jorge E.; Alvarez, Diego A.: The encounter of interval and probabilistic approaches to structural reliability at the design point (2012)
  15. Li, Jian; Wang, Hai; Kim, Nam H.: Doubly weighted moving least squares and its application to structural reliability analysis (2012) ioport