Braun, MoritzMoritzBraunKellner, LeonLeonKellnerSchreiber, SarahSarahSchreiberEhlers, SörenSörenEhlers2022-06-022022-06-022021-11Procedia Structural Integrity 38 (C) : 182-191 (2022)http://hdl.handle.net/11420/12778Butt-welded joints are common in many industries. The fatigue behavior of such joints depends on numerous factors, e.g. load level, local weld geometry, or parent material strength. To make things worse, these factors often interact, yet mutual influence can hardly be quantified by multivariate studies, i.e. varying one factor at a time out of many factors, due to the large number of required tests and the statistical nature of weld geometry. Consequently, fatigue assessment of such joints often deviates significantly between prediction and experimental result. Thus, alternative methods are desirable in order to take various influencing factors into account. To this end, machine learning techniques were used to predict failure locations and number of cycles to failure of fatigue tests performed on small-scale butt-welded joint specimens. In addition to accurate predictions, an understanding of importance and mutual influence of the factors is desired, e.g. a ranking of the most important factors; however, capturing the influence of several possibly interacting factors usually requires complex nonlinear machine learning models. We used gradient boosted trees. Since these are black box models, the SHapley Additive exPlanations (SHAP) framework was used to explain the predictions, i.e. identify influential features and their interactions. Lastly, the model explanations are linked back to domain knowledge.en2452-32162021C182191Elsevierhttps://creativecommons.org/licenses/by-nc-nd/4.0/explainable AIFatigue life predictionFatigue strengthgradient boosted treesMachine learning modelsSHAPWelded jointsMLE@TUHHTechnikJournal Article10.15480/882.435710.1016/j.prostr.2022.03.01910.15480/882.4357Journal Article