Prediction of uneven distribution of snow load based on generalized regression neural network

At present, there are mainly three research ways on the distribution of snow load: field measurement, experimental simulation, and numerical simulation. They all need expensive equipment, specimen model used only once, and slight defective simulation software. Artificial Neural Networks (ANNs) rely on a large number of input functions to estimate unknown or known occurrences. This paper applies ANNs to predict the uneven distribution of snow loads. First, the snow load database is collected and set up, including all the data from field measurement and experimental simulation. Second, part of the data from the 1m³ model is used as a training set to train Backpropagation Neural Networks (BPNN) and Generalized Regression Neural Networks (GRNN). The parameters of the two neural networks are determined (such as the smoothing factor in the GRNN), and the rest of the data is used to examine the model; the prediction and measurement results of the two models are compared. Finally, based on different wind direction angles and wind speed, a GRNN model is established to predict the uneven snow load distribution. Through comparative analysis, GRNN is more suitable for predicting the uneven distribution of snow loads, especially in predicting snow thickness. In particular, the prediction effect of the model on the snow load distribution trend is good, and the prediction ability of the neural network model is displayed.