Analysis of snow load probabilistic models and calculation of reference snow pressure using maximum likelihood method for multiple cities in Liaoning Province, China

Abstract

The accurate determination of snow loads is crucial for structural design, and determining the reference snow pressure is particularly important for structures vulnerable to snow. The Load Code for the Design of Building Structures (GB50009–2012) provides a standard probabilistic model and fitting method to facilitate the calculation of reference snow pressure during the design reference period. However, using a unified load probabilistic distribution model across different provinces in China may result in overestimation or underestimation of reference snow pressure due to variations in geographic locations. Therefore, this study takes Liaoning Province, including all its cities as an example, which is the only province in northeast China that is both coastal and inland. Measured data from the Global Land Data Assimilation System (GLDAS) were used to establish the extreme value type I distribution, extreme value type III distribution, lognormal distribution, and generalized extreme value distribution models for snow depth using maximum likelihood and moment methods. The Kolmogorov–Smirnov test was used to evaluate the goodness-of-fit of the established models, and the maximum likelihood method was compared with the fitting method (moment method) recommended by the Load Code for the Design of Building Structures. The results showed that the lognormal distribution model and the maximum likelihood method achieved the best goodness-of-fit for the probabilistic distribution of snow depth. Additionally, the influence of snow density uncertainty on reference snow pressure was analyzed using measured data for snow pressure and snow depth.