Fatigue behavior of welded joints is significantly influenced by numerous factors, for example, local weld geometry. A representative quantity for the influence of the notch effect created by the local weld geometry is the stress concentration factor (SCF). Thus, SCFs are often used to estimate fatigue failure locations and fatigue strength; however, this simplifies the mutual effect of other influencing factors. Consequently, fatigue strength estimates for welded joints may deviate from experimental results. Machine learning techniques offer an alternative to traditional fatigue assessment approaches based on SCFs. This study presents a comparison of failure location predictions and number of cycles to failure for 621 fatigue tests of small-scale butt-welded joints. In addition, an understanding of importance and mutual influence of the factors is desired. We used gradient boosted trees in combination with the SHapley Additive exPlanation framework to identify influential features and their interactions.
