Structural and Multidisciplinary Optimization

, Volume 58, Issue 3, pp 955–968 | Cite as

Global kriging surrogate modeling for general time-variant reliability-based design optimization problems

  • Lara Hawchar
  • Charbel-Pierre El Soueidy
  • Franck Schoefs


While design optimization under uncertainty has been widely studied in the last decades, time-variant reliability-based design optimization (t-RBDO) is still an ongoing research field. The sequential and mono-level approaches show a high numerical efficiency. However, this might be to the detriment of accuracy especially in case of nonlinear performance functions and non-unique time-variant most probable failure point (MPP). A better accuracy can be obtained with the coupled approach, but this is in general computationally prohibitive. This work proposes a new t-RBDO method that overcomes the aforementioned limitations. The main idea consists in performing the time-variant reliability analysis on global kriging models that approximate the time-dependent limit state functions. These surrogates are built in an artificial augmented reliability space and an efficient adaptive enrichment strategy is developed that allows calibrating the models simultaneously. The kriging models are consequently only refined in regions that may potentially be visited by the optimizer. It is also proposed to use the same surrogates to find the deterministic design point with no extra computational cost. Using this point to launch the t-RBDO guarantees a fast convergence of the optimization algorithm. The proposed method is demonstrated on problems involving nonlinear limit state functions and non-stationary stochastic processes.


Reliability-based design optimization Time-variant reliability Kriging surrogate model Active-learning Stochastic process 


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Lara Hawchar
    • 1
  • Charbel-Pierre El Soueidy
    • 1
  • Franck Schoefs
    • 1
  1. 1.Institute of Research in Civil and Mechanical Engineering (GeM - UMR 6183)Université de NantesNantesFrance

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