Data-driven fatigue assessment of welded steel joints based on transfer learning

Data-driven or machine learning (ML) approaches already achieved significant success in many engineering areas even fatigue assessment of industrial parts and structures. Machine learning approaches work well under the common assumption that the training data covers the relevant feature space of the application. Rebuilding new models or establish new databases for similar feature spaces needs a high effort. In such cases, knowledge transfer or transfer learning can be used. In this study, transfer learning approach is used to determine the fatigue life of welded steel joints (target task) with a ML-algorithm that is trained in non-welded steel specimen. Twenty-two fatigue test data series were used. The results of the transfer learning approach were compared with a conventional machine learning approach that was trained also on data of welded joints. Furthermore, the results were compared to an advanced analytical approach (IBESS) for the fracture mechanic-based fatigue life assessment of welded joints and fatigue strength values from recommendations.