An Artificial Neural Network Model for the Stress Concentration Factors in KT-Joints Subjected to Axial Compressive Load

Abstract

Stress concentration factor (SCF) is usually used to estimate the fatigue life of an offshore joint. Historically, parametric equations were used to estimate SCF based on a statistical analysis of experimental and finite element analysis (FEA) results, to reduce cost and time. These equations give the SCF at the saddle/crown position for simple joints and basic load cases. However, for modified or defective joints, the location of the maximum SCF can change. In such circumstances, the single-point SCF equation cannot be used to estimate the maximum value of SCF, as its location may have changed from saddle/crown. To our knowledge, there are no general expressions to estimate SCF around the brace axis accurately. As artificial neural networks (ANN) can approximate the trend of complex phenomena better than conventional data fitting, a mathematical model based on ANN is proposed to estimate SCF based on the weights and biases of trained ANN. Nine hundred thirty-seven finite element simulations were performed to generate SCF data for training the ANN. This ANN was used to model an empirical equation for SCF. The proposed empirical model can estimate SCF around the brace axis with less than 5% error. The current study provides a roadmap to using FEA and ANN for empirical modeling of SCF in tubular joints, and this approach can be applied to any joint type, with or without design modification or damage. Once a database of similar equations is available, it can be utilized for quickly estimating SCF instead of costly experimentation and FEA. Optimization of the ANN can further improve the accuracy of the developed mathematical model.