A Bayesian framework for calibration of multiaxial fatigue curves
Data
2022Language
en
Soggetto
Abstract
A Bayesian framework is proposed to re-formulate a multiaxial fatigue model and produce probabilistic stress-life fatigue curves from experimental data. The proposed framework identifies the experimentally-driven parameters governing the multiaxial fatigue model, in the form of probability distributions. Classical and hierarchical Bayesian inference strategies are presented, accompanied by rigorous analytical expressions for calculating the joint posterior distributions necessary for in-field implementation. An example illustrates the application of the proposed hierarchical Bayesian inference framework and how it compares to a deterministic approach. This probabilistic treatment makes the existing fatigue models suitable for exercising uncertainty propagation for reliability analysis and design purposes. © 2022
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