The use of jacket structures to support offshore installations has for a long time been a popular choice in places with appropriate water depths. In recent years the use of jacket structures as offshore wind turbine foundations has also attracted increasing attention and is becoming a feasible alternative to traditional monopile foundations.

One of the key challenges in jacket design is optimizing tubular joints in terms of fatigue resistance. As it is not practically possible to include detailed FEM joint models in global jacket models designers are forced to look for alternative methods to obtain realistic joint representations. This is done by calculating influence factors (INF) and stress concentration factors (SCF) to be applied to simplified models of relevant tubular joints in global models in order to achieve a realistic force flow in the structure. One simple and widely used method is to apply parametric formulas, e.g. those suggested by Efthymiou. However, these approximating formulas have a fairly limited validity range. Therefore, on complex joint the most reliable way to determine INF’s is by setting up refined FEM models of relevant joints and then subsequently using the calculated factors in the global model. This strategy is computationally demanding and hence, very time consuming, as a new detailed FEM analysis of the tubular joint must be conducted for each step in the optimization process.

The present paper demonstrates how this time consuming procedure can be avoided by use of artificial neural networks (ANN) trained to estimate INF’s on tubular joints. The neural network is trained on a pre-generated library of detailed FEM joint models and is then able to predict INF’s on joints that are not part of the library — and thereby providing a significant reduction in calculation time during the jacket/joint optimization process. The analysis is conducted on a typical joint on a three legged jacket structure. The joint is located on a jacket leg and has two incoming braces. Such a joint has a finite number of free design variables, e.g. chord diameter/thickness, brace diameter/thickness, brace angle, gap etc. Each of these free variables can be considered as a dimension in the joint design space. Having a sufficient number of FEM joint models in the library the neural network can be trained to recognize and predict underlying patterns in this design space. The method is demonstrated on a limited number of design variables but should easily be extended to cover all variables as the joint library is expanded to include all dimensions.

This content is only available via PDF.
You do not currently have access to this content.