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Application of adaptive surrogate models in time-variant fatigue reliability assessment of welded joints with surface cracks

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  • Dong, Y.
  • Teixeira, A.P.
  • Guedes Soares, C.

Abstract

The time-variant fatigue reliability of welded joints subjected to stochastic loading is assessed based on the PHI2 method, which takes the time-variant reliability problem as a two-component parallel system reliability problem. The complex fatigue process that takes place at the weld toe is modeled as a semi-elliptical surface crack growth process. Surrogate models, representing surface crack sizes at a time instant, are applied to solve the reliability problem involving time-consuming fatigue crack growth analyses. Polynomial regression models and Kriging interpolation models are both employed. Their corresponding adaptive procedures are also adopted to improve the reliability results. Some adjustments of the active refinement algorithm based on the Kriging model are proposed to make it more suitable for the present study. The time-variant fatigue reliability assessment of a T-plate welded joint is carried out. Without using the adaptive procedures, the Kriging models can better represent the crack sizes compared with the polynomial models. The outcrossing rate formulated by the PHI2 method is sensitive to the reliability index of the first component of the two-component parallel system. The effectiveness of the adaptive procedures, which improve the reliability results, is demonstrated.

Suggested Citation

  • Dong, Y. & Teixeira, A.P. & Guedes Soares, C., 2020. "Application of adaptive surrogate models in time-variant fatigue reliability assessment of welded joints with surface cracks," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
  • Handle: RePEc:eee:reensy:v:195:y:2020:i:c:s095183201930420x
    DOI: 10.1016/j.ress.2019.106730
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    References listed on IDEAS

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    1. Chao Hu & Byeng D. Youn & Pingfeng Wang, 2019. "Engineering Design under Uncertainty and Health Prognostics," Springer Series in Reliability Engineering, Springer, number 978-3-319-92574-5, December.
    2. Sun, Zhili & Wang, Jian & Li, Rui & Tong, Cao, 2017. "LIF: A new Kriging based learning function and its application to structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 152-165.
    3. Dong, Y. & Teixeira, A.P. & Guedes Soares, C., 2018. "Time-variant fatigue reliability assessment of welded joints based on the PHI2 and response surface methods," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 120-130.
    4. Echard, B. & Gayton, N. & Lemaire, M. & Relun, N., 2013. "A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 232-240.
    5. Gaspar, B. & Teixeira, A.P. & Guedes Soares, C., 2017. "Adaptive surrogate model with active refinement combining Kriging and a trust region method," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 277-291.
    6. Dong, Wenbin & Moan, Torgeir & Gao, Zhen, 2012. "Fatigue reliability analysis of the jacket support structure for offshore wind turbine considering the effect of corrosion and inspection," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 11-27.
    7. Wen, Zhixun & Pei, Haiqing & Liu, Hai & Yue, Zhufeng, 2016. "A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 170-179.
    8. Du, Weiqi & Luo, Yuanxin & Wang, Yongqin, 2019. "Time-variant reliability analysis using the parallel subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 250-257.
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