Reliability evaluation of stochastic fatigue crack growth in welded joints of orthotropic steel decks based on site-specific traffic data

Fatigue crack growth (FCG) in welded joints of orthotropic steel decks (OSDs) is highly stochastic, which complicates the accurate assessment of fatigue reliability. To consider the stochastic factors affecting fatigue performance in welded joints, this study proposed a stochastic FCG analysis method that incorporates machine learning to evaluate the fatigue reliability. Firstly, a probabilistic model for fatigue stress spectrum in welded joints was developed using site-specific traffic data. Subsequently, stochastic FCG analysis method assisted by a random forest model was developed to replace the time-consuming finite element solution. Finally, a fracture limit state function was established to evaluate fatigue reliability with consideration of the stochastic FCG process. Results indicate that a lognormal distribution accurately fits fatigue characteristic of the equivalent stress intensity factor range, with mean values consistently lower at the crack tip compared to the crack edge. Additionally, the deterioration rate of welded joints accelerates significantly at a crack depth of 5 mm, resulting in a shift of the remaining fatigue life distribution from lognormal to exponential. The reliability indices decrease significantly under overloading conditions. A 20% increase in vehicle weight reduces the fatigue life from the expected 100 years to 82 years.