The stepwise accuracy-improvement strategy based on the Kriging model for structural reliability analysis
- 23 Downloads
For structural reliability analysis with time-consuming performance functions, an innovative design of experiment (DoE) strategy of the Kriging model is proposed, which is named as the stepwise accuracy-improvement strategy. The epistemic randomness of the performance value at any point provided by the Kriging model is used to derive an accuracy measure of the Kriging model. The basic idea of the proposed strategy is to enhance the accuracy of the Kriging model with the best next point that has the largest improvement with regard to the accuracy measure. An optimization problem is developed to define the best next point. The objective function is the expectation that quantifies how much an untried point could enhance the accuracy of the Kriging model. Markov chain Monte Carlo sampling and Gauss–Hermite quadrature are employed to make several approximations to solve the optimization problem and get the best next point. A structural reliability analysis method is also constructed based on the proposed strategy and the accuracy measure employed. Several examples are studied. The results validate the advantages of the proposed DoE strategy.
KeywordsStructural reliability analysis Design of experiments The adaptive Kriging model Monte Carlo simulation
- Melchers RE (1990) Radial importance sampling for structural reliability. Jof Engrgmechasce 116(1):189–203Google Scholar
- Salemi P, Nelson BL, Staum J (2016) Moving least squares regression for high-dimensional stochastic simulation metamodeling. ACM Trans Model Comput Simul 26(3):1–25Google Scholar
- Schöbi R, Sudret B (2014) Combining polynomial chaos expansions and Kriging for solving structural reliability problems. In: Spanos P, Deodatis G, (eds). Proceedings of the 7th international conference on computational stochastic mechanics (CSM7). Santorini, GreeceGoogle Scholar
- Zhang Y et al (2015) An efficient Kriging method for global sensitivity of structural reliability analysis with non-probabilistic convex model. Proc Inst Mech Eng O J Risk Reliab 229(5):442–455Google Scholar