For the frequency-domain spectral fatigue analysis, the probability mass function of stress range is essential for the assessment of the fatigue damage. The probability distribution of the stress range in the narrow-band process is known to follow the Rayleigh distribution, however the one in the wideband process is difficult to define with clarity. In this paper, in order to assess the fatigue damage of a structure under wide band excitation, the probability mass function of the wide band spectrum was derived based on the artificial neural network, which is one of the most powerful universal function approximation schemes. To achieve the goal, the multi-layer perceptron model with a single hidden layer was introduced and the network parameters are determined using the least square method where the error propagates backward up to the weight parameters between input and hidden layer. To train the network under supervision, the varieties of different wide-band spectrums are assumed and the probability mass function of the stress range was derived using the rainflow counting method, and these artificially generated data sets are used as the training data. It turned out that the network trained using the given data set could reproduce the probability mass function of arbitrary wide-band spectrum with success.
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