An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models

  • Jafar Vahedi Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan
  • Mohammad Reza Ghasemi Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan
  • Mahmoud Miri Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan

Abstract

Meta-models or surrogate models are convenient tools for reliability assessment of problems with time-consuming numerical models. Recently, an adaptive method called AK-MCS has been widely used for reliability analysis by combining Mont-Carlo simulation method and Kriging surrogate model. The AK-MCS method usually uses constant regression as a Kriging trend. However, other regression trends may have better performance for some problems. So, a method is proposed by combining multiple Kriging meta-models with various trends. The proposed method is based on the maximum entropy of predictions to select training samples. Using multiple Kriging models can reduce the sensitivity to the regression trend. So, the propped method can have better performance for different problems. The proposed method is applied to some examples to show its efficiency.

Keywords: reliability, meta-model, simulation, Kriging, adaptive method
Published online
2019-03-19
How to Cite
Vahedi, J., Ghasemi, M. R., & Miri, M. (2019). An Efficient Entropy-Based Method for Reliability Assessment by Combining Kriging Meta-Models. Periodica Polytechnica Civil Engineering, 63(2), 414-422. https://doi.org/10.3311/PPci.12747
Section
Research Article