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http://dx.doi.org/10.12989/sem.2019.69.4.361

A new Bayesian approach to derive Paris' law parameters from S-N curve data  

Prabhu, Sreehari Ramachandra (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST))
Lee, Young-Joo (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST))
Park, Yeun Chul (Institute of Construction and Environmental Engineering, Seoul National University)
Publication Information
Structural Engineering and Mechanics / v.69, no.4, 2019 , pp. 361-369 More about this Journal
Abstract
The determination of Paris' law parameters based on crack growth experiments is an important procedure of fatigue life assessment. However, it is a challenging task because it involves various sources of uncertainty. This paper proposes a novel probabilistic method, termed the S-N Paris law (SNPL) method, to quantify the uncertainties underlying the Paris' law parameters, by finding the best estimates of their statistical parameters from the S-N curve data using a Bayesian approach. Through a series of steps, the SNPL method determines the statistical parameters (e.g., mean and standard deviation) of the Paris' law parameters that will maximize the likelihood of observing the given S-N data. Because the SNPL method is based on a Bayesian approach, the prior statistical parameters can be updated when additional S-N test data are available. Thus, information on the Paris' law parameters can be obtained with greater reliability. The proposed method is tested by applying it to S-N curves of 40H steel and 20G steel, and the corresponding analysis results are in good agreement with the experimental observations.
Keywords
Bayesian approach; fatigue crack growth; Paris' law; statistical parameter; S-N curve;
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