Browse > Article
http://dx.doi.org/10.3744/SNAK.2019.56.2.161

Stochastic Fatigue Life Assesment based on Bayesian-inference  

Park, Myong-Jin (Department of Naval Architecture and Ocean Engineering, INHA University)
Kim, Yooil (Department of Naval Architecture and Ocean Engineering, INHA University)
Publication Information
Journal of the Society of Naval Architects of Korea / v.56, no.2, 2019 , pp. 161-167 More about this Journal
Abstract
In general, fatigue analysis is performed by using deterministic model to estimate the optimal parameters. However, the deterministic model is difficult to clearly describe the physical phenomena of fatigue failure that contains many uncertainty factors. With regard to this, efforts have been made in this research to compare with the deterministic model and the stochastic models. Firstly, One deterministic S-N curve was derived from ordinary least squares technique and two P-S-N curves were estimated through Bayesian-linear regression model and Markov-Chain Monte Carlo simulation. Secondly, the distribution of Long-term fatigue damage and fatigue life were predicted by using the parameters obtained from the three methodologies and the long-term stress distribution.
Keywords
Long-term fatigue damage; Stochastic fatigue life assessment; Probabilistic S-N curve; Bayesian inference; Bayesian linear regression; Markov chain monte carlo simulation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Dong, P., 2003. Battelle Structural Stress JIP Final Report. N004431-01.
2 Guida, M. & Penta F., 2009. A Bayesian analysis of fatigue data. Structural Safety, 32(1), pp.64-76.   DOI
3 Hamada, M.S., Wilson, A., Reese, C.S. & Martz, H.F., 2008. Bayesian reliability. 1st Ed. Springer: New York.
4 Lee, J.O., Lee, H.Y., Suh, Y.S. & Yoon, J.H., 1998. Reliability of fatigue life predictions for fixed offshore structures. Journal of the Society of Naval Architects of Korea, 35(2), pp.74-82.
5 Liu, X.W., Lu, D.G & Hoogenboom P.C.J., 2017. Hierarchical Bayesian fatigue data analysis. International Journal of Fatigue, 100(1), pp.418-428.   DOI
6 Llera, A. & Beckmann, C.F., 2016. Estimating an inverse gamma distribution. Donders Institute Technical Report arXiv:1605.01019v2.
7 Marin, J.M. & Robert, C.P., 2007. Bayesian core:a practical approach to computational Bayesian statistics. 1st Ed. Springer: New York.
8 Roberts, G.O. & Rosenthal, J.S., 2001. Optimal scaling for various metropolis-hastings algorithms. Statistical Science, 16(4), pp.351-367.   DOI
9 Rose, C. & Smith, M.D., 2002. Mathematical statistics with mathematica. 1st Ed. Springer: New York.
10 Ruano, J.C., 2014. A Bayesian approach to fatigue damage assessment in composite materials. Ph.D. University of Granada.
11 Kang, S.W. et al., 2004. Testing and analysis of fatigue behavior in edge details. Proceedings of OMAE Specialty Conference on Integrity of Floating Production, Storage & Offloading (FPSO) Systems, Houston, Texas, USA, 30 August - 2 September, 2004, NO. 04-0025.
12 Yang, P.D.C., Lee, J.S., Yoon, J.H. & Seo, Y.S., 1997. Fatigue strength analysis and reliability analysis of D/H VLCC. Journal of the Society of Naval Architects of Korea, 34(2), pp.64-74.
13 Yang, Y.S. & Yoon, J.H. 1991. A study on the fatigue reliability of structures by Markov Chain model. Transactions of the Society of Naval Architects of Korea, 28(2), pp.228-240.