4.7 Article

Probabilistic method for fatigue crack growth prediction with hybrid prior

期刊

INTERNATIONAL JOURNAL OF FATIGUE
卷 157, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.ijfatigue.2021.106686

关键词

Fatigue life prediction; Bayesian inference; Monte Carlo integration; Prior distribution fusion

资金

  1. NSERC Discovery Grants Program [RGPIN-2017-04408]
  2. National Research Council of Canada through the Canada-Germany 3+2 Joint Project Digital Twin Platform for Infrastructure Asset Lifecycle Management

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This paper proposes a new probabilistic prediction method that improves the prediction accuracy of fatigue crack growth by integrating multiple priors.
In practical engineering, there is usually multi-source prior knowledge which can be integrated for fatigue crack growth (FCG) prediction. This paper proposes a new probabilistic prediction method which enables the input of hybrid prior. This method comprises two inference steps. In the first inference step, a set of candidate priors are input. Then, the Monte Carlo integration is adopted in the calculation of posterior belief of each candidate prior. In the second inference step, the particle filter is extended to conduct Bayesian inference with hybrid prior. Numerical studies show integrating multiple priors can increase the robustness of FCG prediction.

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