Peak Factors for Non-Gaussian Load Effects Revisited

The estimation of the extreme of non-Gaussian load effects for design applications has often been treated tacitly by invoking a conventional peak factor based on Gaussian processes. This assumption breaks down when the loading process exhibits strong nonGaussianity, where a conventional peak factor yields relatively nonconservative estimates due to failure to include long tail regions inherent to non-Gaussian processes. In order to realistically capture the salient characteristics of non-Gaussian load effects and reflect these in the estimates of their extremes, this study examines the peak factor for non-Gaussian processes, which can be used for estimating the expected value of the positive and negative extremes of non-Gaussian load effects. The efficacy of previously introduced analytical expressions for the peak factor of non-Gaussian processes based on a moment-based Hermite model is evaluated and the variance of the estimates in terms of standard deviation is derived. In addition, some improvements to the estimation of the peak factor and its standard deviation are discussed. Examples including immediate applications to other areas illustrate the effectiveness of this model-based peak factor approach.