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Structural fatigue reliability analysis based on active learning Kriging model
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AI
DOI：10.1016/j.ijfatigue.2023.107639.png)
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Abstract
En 中文
The paper introduces the active learning Kriging (ALK) model into the structural fatigue reliability analysis. Firstly, the structural variable stress is obtained by experimental tests or finite element simulation (FES). On this basis, the cyclic stress corresponding to the fatigue life is analyzed based on the rain-flow counting method and the structural fatigue life is correspondingly computed using the Miner-Palmgren damage rule. Secondly, the uncertainties to affect the structural variable stress are considered and thus the prediction of structural fatigue lives can be obtained. Further, the structural fatigue reliability model is established, and its reliability is obtained by computing the probability that the predicted fatigue lives are greater than the allowable life. Finally, to balance the accuracy and efficiency for computing the structural fatigue reliability, a small number of boundary sample points for experiment or FES are produced and the corresponding fatigue lives are computed. Sequen-tially, the Kriging model is adopted to approximate the structural fatigue reliability model and it is adaptively updated by the active learning strategy. Several examples are also given to demonstrate the effectiveness of the proposed ALK-based structural fatigue reliability method.
Keywords:
active learning Kriging
Rain-flow counting method
Miner-Palmgren damage rule
Fatigue life
Reliability analysis
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