In order to ensure high weld qualities and structural integrity of engineering structures, it is crucial to detect areas of high stress concentrations along weld seams. Traditional inspection methods rely on visual inspection and manual weld geometry measurements. Recent advances in the field of automated measurement techniques allow virtually unrestricted numbers of inspections by laser measurements of weld profiles; however, in order to compare weld qualities of different welding processes and manufacturers, a deeper understanding of statistical distributions of stress concentrations along weld seams is required. Hence, this study presents an approach to statistically characterize different types of butt joint weld seams. For this purpose, an artificial neural network is created from 945 finite element simulations to determine stress concentration factors at butt joints. Besides higher quality of predictions compared to empirical estimation functions, the new approach can directly be applied to all types welded structures, including arc- and laser-welded butt joints, and coupled with all types of 3D-measurement devices. Furthermore, sheet thickness ranging from 1 mm to 100 mm can be assessed.

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The environment in cold regions undergoes significant changes that manifest in rising temperatures and melting ice caps. These processes allow access to new areas for shipping and the installation of structures. However, the occurring changes are not solely a reduction of ice, but also waves increasingly occur in cold regions contributing to ice break up in the Marginal Ice Zone (MIZ) and the transport of ice towards the open sea. Much work has been done to combine the single topic disciplines: wave-hydrodynamics, ice mechanics and structure mechanics to wave-structure (WSI) and ice-structure interaction (ISI). The changing environment in cold regions and the increased wave activity form a new combined discipline: wave-ice-structure interaction (WISI). This paper addresses existing knowledge gaps of the future loading scenario WISI that need to be addressed in engineering to ensure safety for future operations in Polar Regions. The knowledge gaps however, do not only refer to the discipline interfaces, i.e. challenges in combining them, but also to knowledge gaps within them. Wave statistics and the cross-effect between wave and ice are widely unknown which limits the definition of a design wave-scenario. Structures in such environments are exposed to subzero temperatures and neither their impact on properties nor on fatigue life is fully understood. While most phenomena of these two disciplines and their implementation into numerical models are established the ice mechanics appear as the weakest link. Ice is a complex material and not all aspects of its mechanical behaviour are understood and if – the implementation into (numerical) models has not been successful yet. Ice pieces that are energetically charged by waves and collide with structures at high velocities and for such high impact loads the governing ice mechanics are hardly covered by the state of the art or not at all.

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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.