Snow load models for probabilistic optimization of steel frames

Three probabilistic snow load models based on the Poisson spike process and the block maxima method are derived from the second moment probabilistic description of monthly extreme values of snow load. For comparison of the models, the upper tail asymptotic approximations of their cumulative probability distribution functions of yearly maxima are obtained. The performance of the models is checked by the probabilistic design and verification of representative portal frames of low-rise industrial buildings exposed to snow and wind loads using the first-order reliability method (FORM). The statistical parameters of monthly maxima of climatic loads related to measurements at six locations in Germany are employed. The considered snow load has a pattern of a seasonal occurrence of snowfall. The Poisson spike process model is then applied to the probabilistic optimization of safety factors for the standardized design of frames. A novel optimization of variable safety factors is proposed to differentiate design for frames with light to heavy weight roofs. It is shown that this differentiation significantly reduces scatter of the reliability level around the target value.