Using Bootstrap to Assess Sampling Uncertainty in Fatigue Crack Growth Life

Bhachu, Kanwardeep S.; Haftka, Raphael T.; Kim, Nam Ho

doi:10.2514/6.2015-0662

Cited by 4 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiple samples

θ ((), F)

can be generated to estimate the statistical distribution of the unknown parameters

θ

and their corresponding eigenvalues. For further details on the bootstrap method, readers may refer to previous studies 41,42 …”

Section: Improved Bootstrap Methodsmentioning

confidence: 99%

“…For further details on the bootstrap method, readers may refer to previous studies. 41,42 Although the bootstrap method has become a powerful statistical tool, its limitations are apparent and can be summarized as follows. 43,44 First, the lack of independent observations is a major issue in the bootstrap due to the duplication of observations in the resampling procedure.…”

Section: Bootstrap Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty for fatigue life of low carbon alloy steel based on improved bootstrap method

Liu

et al. 2023

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

Fatigue testing is a crucial technique for assessing the fatigue performance of materials and components. However, the testing process often involves numerous uncertainties, such as variations in part roughness and size, and the collected data typically constitutes small samples. This paper proposes an improved bootstrap method with cycle of uncertainty to address the limitations of traditional bootstrap methods in estimating uncertainties in small sample sizes. The proposed method was applied to estimate the effect of surface roughness and common normal size on fatigue tests, resulting in improved estimation accuracy. The research contributes to the development of improved methods for fatigue life estimation, which has practical implications for mechanical component design and optimization.

show abstract

“…Multiple samples

θ ((), F)

can be generated to estimate the statistical distribution of the unknown parameters

θ

and their corresponding eigenvalues. For further details on the bootstrap method, readers may refer to previous studies 41,42 …”

Section: Improved Bootstrap Methodsmentioning

confidence: 99%

Section: Bootstrap Methodsmentioning

confidence: 99%

Uncertainty for fatigue life of low carbon alloy steel based on improved bootstrap method

Liu

et al. 2023

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

show abstract

“…The method has been applied to mechanical engineering (Yaghoubi et al, 2018), aerospace (Wang et al, 2014), hydrology (Zhang et al, 2014), biology (Larsen et al, 2006), and other fields. The bootstrap method has also been used in the study of fatigue life and reliability of materials (Bhachu et al, 2015;Wang et al, 2017a). Additional information on the bootstrap method can be reviewed in (Kijewski and Kareem, 2002;Kreiss and Paparoditis, 2011).…”

Section: Introductionmentioning

confidence: 99%

Parameter distribution characteristics of material fatigue life using improved bootstrap method

Zhang

Liu

Wang

et al. 2018

International Journal of Damage Mechanics

View full text Add to dashboard Cite

The bootstrap method is mostly used to estimate statistical characteristics of small sample data. However, the limitations of the bootstrap method itself lead to a reduction in the reliability of small-sample estimates. In this article, an improved bootstrap method is developed to address this problem. In the statistically significant error range (the sample average error and the limit error of sampling) of the original single sample data, expanding the virtual test data that obey two distributions to overcome the limitations of the bootstrap method itself. This article compares and analyses these two methods through the case; the result indicates that the improved bootstrap method can enhance the reliability of the estimation results without changing its probability distribution. We also discussed how to reduce the fluctuation of the improved bootstrap method. And the effectiveness and feasibility of this improved method are discussed in the analysis of fatigue life test data.

show abstract

Epistemic uncertainty quantification in metal fatigue crack growth analysis using evidence theory

Tang

et al. 2017

International Journal of Fatigue

View full text Add to dashboard Cite

Using Bootstrap to Assess Sampling Uncertainty in Fatigue Crack Growth Life

Cited by 4 publications

References 19 publications

Uncertainty for fatigue life of low carbon alloy steel based on improved bootstrap method

Uncertainty for fatigue life of low carbon alloy steel based on improved bootstrap method

Parameter distribution characteristics of material fatigue life using improved bootstrap method

Epistemic uncertainty quantification in metal fatigue crack growth analysis using evidence theory

Contact Info

Product

Resources

About