Peak Factors for Non-Gaussian Load Effects Revisited

Abstract

The estimation of the extreme of non-Gaussian load effects for design applications has often been treated tacitly by invoking a conventional peak factor on the basis of Gaussian processes. This assumption breaks down when the loading process exhibits non-Gaussianity, in which a conventional peak factor yields relatively nonconservative estimates because of failure to include long tail regions inherent to non-Gaussian processes. To realistically capture the salient characteristics of non-Gaussian load effects and incorporate 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 on the basis of a moment-based Hermite model is evaluated and the variance of the estimates in 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.