Neural network-based estimation of stress concentration factors for steel multiplanar tubular XT-joints

The hot-spot stress method fur fatigue design of tubular joints relies on the accurate predictions of the stress concentration factors (SCF) at the brace to chord intersection areas. At present, SCFs are predicted based on established empirical equations. An alternative approach using a neural network-based model has been developed in this paper to estimate the SCFs of multiplanar tubular XT-joints. The neural network software, Stuttgart Neural Network Simulator, was used for the purpose. To train and test the network, an SCF database was built up using the finite clement method (FEM). The database covers a wide range of geometrical parameters for the XT-joints. Three axial load cases were considered. The geometrical properties of the tubular joints were used as the training input data. The FEM SCFs are used as the training output data. Different network configurations are also tested for the best possible results. The results show that a trained neural network can indeed predict the SCFs for the various load cases with a higher level of accuracy. (C) 2001 Elsevier Science Ltd. All rights reserved.