Fatigue reliability analysis under two-stage loading

Ni, Kan; Sheng-kun, Zhang

doi:10.1016/s0951-8320(00)00009-0

Cited by 23 publications

(10 citation statements)

References 7 publications

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“…Various analytical methods have been developed to estimate the value of integral in Eq. (18). The FORM approach transforms all the random variables to an uncorrelated standard normal space, finds the minimum distance from the limit state to the origin, and estimates the failure probability based on the minimum distance.…”

Section: Methods Ii: Form Approachmentioning

confidence: 99%

“…One approach assumes that fatigue lives at different stress levels are independent random variables (Liao et al, 1995; Kam et al, 1998; Le and Peterson, 1999;Shen et al, 2000;Kaminski, 2002). The other approach assumes that fatigue lives at different stress levels are fully dependent random variables (Shimakawa and Tanaka, 1980;Kopnov, 1993Kopnov, , 1997Pascual and Meeker, 1999; Rowatt and Spanos, 1998;Ni and Zhang, 2000;Zheng and Wei, 2005). Liu and Mahadevan (2007) proposed a stochastic S-N curve representation technique to include the actual correlation of fatigue lives across different stress levels.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Methods for Time-Dependent Fatigue Reliability Analysis

Liu

Mahadevan

2009

AIAA Journal

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Two efficient methods for time-dependent fatigue reliability analysis are proposed in this paper based on a random process representation of material fatigue properties and a nonlinear damage accumulation rule. The first method is developed by matching the first two central moments of the accumulated damage to a well-known probability distribution, thus facilitating direct analytical solution of the time-dependent fatigue reliability. The second method uses the first-order reliability method (FORM) to calculate the reliability index based on a time-dependent limit state function. These two methods represent different trade-offs between accuracy and computational efficiency. The proposed methods include the covariance structure of the stochastic damage accumulation process under variable amplitude loading. A wide range of fatigue data available in the literature is used to validate the proposed methods, covering several different types of metallic and composite materials under different variable amplitude loading.

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Section: Methods Ii: Form Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Methods for Time-Dependent Fatigue Reliability Analysis

Liu

Mahadevan

2009

AIAA Journal

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“…where the subscripts 1, 2, 3 1,   i i are the sequence numbers of the loading, 1 Thus, the damage accumulation model under two-level loading can be described as 1 2…”

Section: Non-linear Fatigue Damage Accumulation Model Based On Damentioning

confidence: 99%

“…Fatigue life prediction subjected to variable amplitude loading is a complex problem in engineering practice, and the most important and rudimentary problem of fatigue-life prediction is fatigue damage accumulation under two-level loading [1].…”

Section: Introductionmentioning

confidence: 99%

Notice of Retraction A modified non-linear damage accumulation model considering load interaction effects under two-level loading

Gao

Zuo

Huang

et al. 2013

2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)

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Many structures are subjected to variable amplitude loading in engineering practice. The foundation of fatigue life prediction under variable amplitude loading is how to deal with the fatigue damage accumulation in the case of two-level loading. A non-linear fatigue damage accumulation model which can explain the influence of loading sequences was proposed in earlier literature, but the model cannot consider the load interaction effect. A modified non-linear damage accumulation model is proposed in this paper to account for the loading interaction effects. Experimental data of two metallic materials are used to validate the proposed model. The agreement between model predictions and experimental data is observed, especially under high-low loading conditions.

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“…A perturbation-based stochastic finite element method is proposed (Kaminski 2002) for fatigue analysis of composites, in which the restrictions on the input random variables describing fatigue life are low, since only the moments of the individual random variables are required in the computational methodology, such as mean and variance. The other widely used approach is to use a family of fully correlated S-n curves corresponding to different survival probabilities of the material (Shimokawa and Tanaka 1980;Rowatt and Spanos 1998;Pascual and Meeker 1999;ni and Zhang 2000;Zheng and Wei 2005). This approach may be referred to as the quantile or percentile S-n curve approach (referred to as the Q-S-n curve later in this chapter).…”

Section: Introductionmentioning

confidence: 99%