Probabilistic defect-based modelling of fatigue strength for incomplete datasets assisted by literature data

Probabilistic defect-tolerant fatigue design protocols have become the leading paradigms in structural engineering. To effectively deal with this problem, El Haddad's (EH) curves are generally employed for the evaluation of the fatigue endurance limit. Herein, the synergic exploitation of Logistic Regression (LR) and Maximum a Posteriori (MAP) allows for calibrating EH parameters using the sole data from fatigue characterisation and post-mortem fractography. An extensive literature research provided the ground to introduce, when necessary, prior information for some of the more commonly used metallic alloys. Eventually, EH curves are retrieved upon a Monte Carlo simulation to support probabilistic engineering practice.