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Prediction of Tubular T/Y-Joint SIF by GA-BP Neural Network

  • Structural Engineering
  • Published:
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Abstract

In fracture mechanics, the fatigue evaluation depends on the precise provisions of the stress intensity factor (SIF), which is a quantitative factor established to evaluate the effect of stresses along the crack front. Stress intensity factor performs a definitive role in reflecting material fracture and estimating fatigue life of tubular joints. Some researchers have proposed a series of parametric equations to predict SIF values; however, it gave arguably inaccurate results. There is still a need to improve the accuracy of the SIF prediction’s equation. Thus, to overcome this shortage, this study introduced back-propagation (BP) neural network with conjunction to genetic algorithm (GA) (GA-BP) in predicting the SIF of cracked tubular T/Y-joint. To train and verify the results of the hybrid algorithm GA-BP SIFs’ database covers a wide variety of cracked tubular T/Y joints were simulated by ABAQUS and verified against experimental results. Meanwhile, the SIFs’ result produced by the GA-BP optimization method were compared with those SIF calculated from the accessible parametric equations. The comparison indicated that the GA-BP neural network optimization method is reliable, precise and capable tool in calculating the SIFs of cracked T/Y joints and it also provides higher accuracy than common methods.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 51574198.

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Authors and Affiliations

  1. School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, 610500, China

    Xiao Li, Shier Dong, Hazem Samih Mohamed & Nima Pirhadi

  2. Dept. of Construction and Building Engineering, Egyptian Chinese University, Cairo, 11321, Egypt

    Hazem Samih Mohamed

  3. School of Mechanical and Electrical Engineering, Zaozhuang University, Zaozhuang, 277160, China

    Ghiath Al Aqel

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  1. Xiao Li
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  2. Shier Dong
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  3. Hazem Samih Mohamed
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  4. Ghiath Al Aqel
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  5. Nima Pirhadi
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Correspondence to Hazem Samih Mohamed.

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Li, X., Dong, S., Mohamed, H.S. et al. Prediction of Tubular T/Y-Joint SIF by GA-BP Neural Network. KSCE J Civ Eng 24, 2706–2715 (2020). https://doi.org/10.1007/s12205-020-1200-1

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  • DOI: https://doi.org/10.1007/s12205-020-1200-1

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