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Neural network-based chaotic crossover method for structural reliability analysis considering time-dependent parameters
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AI
DOI：10.1016/j.istruc.2023.05.010.png)
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Abstract
En 中文
Structural reliability analysis with various time-dependent parameters is important for operational safety. To improve the calculation precision and efficiency for structural time-dependent reliability analysis, the neural network-based chaotic crossover method (CCM-NN) is proposed by absorbing extremum thought, artificial neural network, chaotic crossover strategy, reptile search algorithm (RSA), and Bayesian regularization (BR) algorithm. The availability of the CCM-NN method is validated by the time-dependent reliability analysis of aeroengine turbine blisk. The results show that (i) the developed CCM-NN method has superior modeling characteristics, whose modeling time and the average absolute error are 0.36 s and 2.26 x 10-4 m respectively; (ii) the CCM-NN methods holds eminent simulation feature, 0.247 s and 99.98% are simulation time and precision respectively since the Monte Carlo samples is 5 x 103; (iii) the time-dependent reliability of turbine blisk is 0.9987 when the allowable radial deformation of turbine blisk is 1.9215 x 10-3 m. The efforts of this study offer useful insight for structural reliability analysis by considering the effect of dynamic loads, and enrich mechanical reliability theory and method.
Keywords:
Neural network
Chaotic crossover strategy
Turbine blisk
Time-dependent reliability analysis
Journal
IF:
4.3
Papers:
1.2W
Citations:
2.7W