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Publications
Kang, J.C., Sun, L.P. and Guedes Soares, C. (2019), “Fault Tree Analysis of Floating Offshore Wind Turbines”, Renewable Energy, Vol. 133, pp. 1455-1467
The use of surrogate models for time-consuming implicit limit state functions (e.g. requiring a finite element analysis (FEA)) has been a common approach to cope with the computational cost in the context of structural reliability analysis. Well established and efficient methods to compute small failure probabilities such as the first-order reliability method (FORM) or the Monte Carlo based simulation methods with variance reduction techniques can then be applied at low computational cost. Among the presently available surrogate models for these applications (e.g. Bucher & Most 2008, Sudret 2012), the use of Kriging models (also known as Gaussian process models) has emerged recently due to their interesting features for structural reliability analysis, such as the interpolation capability, large flexibility or local adaptability and the prediction uncertainty measure (e.g. Echard et al. 2011, Gaspar et al. 2014). In particular, the prediction uncertainty measure has been explored in the development of efficient adaptive surrogate models based on active refinement algorithms, which provide an active control of the accuracy of the surrogate model and the corresponding failure probability predictions (e.g. Echard et al. 2011, Dubourg et al. 2013).
Typically, such algorithms use learning functions (e.g. U-function proposed by Echard et al. (2011)) to identify the best points of a Monte Carlo sample that will iteratively enrich the sample of support points that define the Kriging surrogate model.
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