Uncertainty analysis of a neural network used for fatigue lifetime prediction

The application of interval set techniques to the quantification of uncertainty in a neural network regression model of fatigue lifetime is considered. Bayesian evidence training was implemented to train a series of multi-layer perceptron networks on experimental fatigue life measurements in glass fibre composite sandwich materials. A set of independent measurements conducted 2 months after the training session, and at intermediate fatigue loading levels, was used to provide a rigorous test of the generalisation capacity of the networks. The robustness of the networks to uncertainty in the input data was investigated using an interval-based technique. It is demonstrated that the interval approach allowed for an alternative to probabilistic-based confidence bounds of prediction accuracy. In addition, the technique provided an alternative network selection tool, and also allowed for an alternative to estimating the lifetime prediction error that was found to be a significant improvement over the Bayesian-derived estimate of confidence bound.