Bayesian analysis of fatigue data with multi-load-level damage accumulation: The benefits of rerun specimens

Väntänen, Miikka; Vaara, Joona; Kemppainen, Jukka; Frondelius, Tero

doi:10.1016/j.ijfatigue.2020.105601

Cited by 1 publication

(2 citation statements)

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“…In the above multiaxial fatigue crack growth model analysis, the uncertainty in the fatigue analysis process is ignored. This uncertainty includes variability of material properties, loading changes, geometric uncertainty, and uncertainty of the applied model 29–40 . These uncertainties are crucial to fatigue analysis and reliability assessment of engineering structures 41,42 .…”

Section: Introductionmentioning

confidence: 99%

“…This uncertainty includes variability of material properties, loading changes, geometric uncertainty, and uncertainty of the applied model. [29][30][31][32][33][34][35][36][37][38][39][40] These uncertainties are crucial to fatigue analysis and reliability assessment of engineering structures. 41,42 Some researchers studied on multiaxial fatigue probabilistic model.…”

Section: Introductionmentioning

confidence: 99%

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Parameter estimation in multiaxial fatigue short crack growth model using hierarchical Bayesian linear regression

Zhao

Yan

2022

Fatigue Fract Eng Mat Struct

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Multiaxial fatigue failure is the most common problem in engineering structures. It is worth noting that short crack growth accounts for most of the fatigue life. Hence, it is necessary to study the short crack growth models for multiaxial fatigue life assessment. The primary focus of this study is to develop a hierarchical Bayesian linear regression method to estimate parameters in multiaxial fatigue crack growth model. The Bayesian method is used to estimate the intercept and slope of the regression equation for each loading path and ensemble test datasets. The method of this work was demonstrated on three multiaxial fatigue crack growth datasets. The main results obtained in this paper were that the parameters of the multiaxial fatigue crack growth model changed significantly with different loading paths, and the parameters of the model depended on the multiaxial loading path.

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Section: Introductionmentioning

confidence: 99%