Structural Engineering and Mechanics

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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)
  • Received : 2018.11.26
  • Accepted : 2019.01.21
  • Published : 2019.02.25
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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

Acknowledgement

Supported by : Korea Agency for Infrastructure Technology Advancement (KAIA)

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