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Crack growth prediction under random loading conditions based on genetic algorithm wavelet neural network
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Ming-yu ZHANG1, Li SUN1, 2, Xiao-ping HUANG1
Journal of Ship Mechanics | 2024, 28(9) : 1430 - 1440
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Journal of Ship Mechanics | 2024, 28(9): 1430-1440
• Structural Mechanics •
Crack growth prediction under random loading conditions based on genetic algorithm wavelet neural network
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Ming-yu ZHANG1, Li SUN1, 2, Xiao-ping HUANG1
Affiliations
  • 1.State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • 2.Marine Design and Research Institute of China, Shanghai 200010, China
Published: 2024-09-20 doi: 10.3969/j.issn.1007-7294.2024.09.013
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Fatigue problem as a common failure form in the engineering field has been widely concerned. The fatigue damage-crack size can be obtained by the fatigue analysis method based on fracture mechanics, but the calculation is relatively complicated. In this paper, aiming at the spectrum analysis based fatigue analysis of ocean engineering structures, the stress intensity factor (SIF) spectrum under random loading conditions of the same hot spot through genetic algorithm wavelet neural network (GAWNN) was established, and the network training with the SIF obtained from finite element analysis was conducted. The results show that the model can predict the SIF spectra under random loading conditions well. The method proposed in this paper can considerably reduce the repetitive finite element calculation and provide a reference for the fatigue life prediction of engineering structures under random load conditions by applying crack propagation method. Finally, combined with the unique crack growth rate curve model, the rapid prediction of crack growth under random loading conditions was realized.

fatigue crack propagation  /  random loading  /  stress intensity factor spectrum  /  wavelet neural network  /  finite element analysis
Ming-yu ZHANG, Li SUN, Xiao-ping HUANG. Crack growth prediction under random loading conditions based on genetic algorithm wavelet neural network[J]. Journal of Ship Mechanics, 2024 , 28 (9) : 1430 -1440 . DOI: 10.3969/j.issn.1007-7294.2024.09.013
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Year 2024 volume 28 Issue 9
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doi: 10.3969/j.issn.1007-7294.2024.09.013
  • Receive Date：2024-03-25
  • Online Date：2026-03-26
  • Published：2024-09-20
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  • Received：2024-03-25
Affiliations
    1.State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    2.Marine Design and Research Institute of China, Shanghai 200010, China
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表12种不同金属材料的力学参数
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Number of
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Number of
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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