Stochastic models of snow load in problems of structural reliability analysis

DOI: 10.37153/2618-9283-2026-2-09-28

Authors:  
Rubric:     Theoretical and experimental studies   
Key words: probabilistic design, snow load, snow cover, reliability, kriging, co-kriging, random variable, stochastic parameter, safety
Annotation:

Introduction. Modeling snow load in a probabilistic context is a complex scientific and technical challenge due to limited statistical information, regional characteristics, and a significant coefficient of variation reaching 95 %. To ensure the reliability of structural objects, accurate snow load models are required, which can be used to predict the maximum snow load over the estimated service life of the structure.

Aim. The research is aimed at developing a probabilistic algorithm for obtaining a statistical sample of data on the weight of snow height in an arbitrary location based on the method of co-kriging and processing data from annual meteorological observations.

Materials and methods. The article proposes using the co-kriging method for spatial interpolation of snow load on the ground based on available weather station data to obtain a statistical sample of data over 50 years in arbitrary geographic coordinates across the Russian Federation. Snow load on the ground is estimated based on the water content of the snow. The resulting statistical data are approximated by a Gumbel probability distribution for further reliability analysis, or a nonparametric 0.98-quantile estimate is performed for semi-probabilistic structural design.

Results. Statistical data from meteorological stations on snow water content over a 50-year period are analyzed, and a visualization of parameter variability is provided, reflecting the

significant variability of snow cover weight and statistical parameters. The proposed algorithm for generating a stochastic snow load on the ground model based on co-kriging demonstrated good convergence during cross-validation with actual values – a 2 % difference compared to the second maximum over 50 years and a 5 % difference in the Gumbel distribution center parameter. Nonparametric approaches, such as the bootstrap method, can be used to estimate only the required quantile (e.g., the 0.98th quantile), allowing for a quantile estimate to be obtained as a confidence interval. An algorithm for constructing a distribution function of snow load on the ground maxima over a period of n years, taking into account climate trends, is proposed based on statistical data simulation.

Conclusions. The proposed approach to snow load on the ground modeling allows for statistical data estimation at any given point for subsequent full probabilistic design of a structural object. Given established trends – both downward and upward – the proposed model can be used to estimate the probability of failure of a structure over a specified period of operation. The conversion from snow load on the ground to snow load on the roof is accomplished through coefficients, which are also random in nature and require probabilistic analysis, as provided in the JCSS probabilistic standard. Additionally, the proposed approach can be used to investigate locations with the greatest data interpolation errors, where additional statistical observations of snow weight are required to generate more accurate stochastic snow load models.   Used Books:

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