PREDICTION OF UNEVEN DISTRIBUTION OF SNOW LOAD BASED ON GENERALIZED REGRESSION NEURAL NETWORK
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Title PREDICTION OF UNEVEN DISTRIBUTION OF SNOW LOAD BASED ON GENERALIZED REGRESSION NEURAL NETWORK
Creator ZHICHENG REN, QINGWEN ZHANG
Contributor Didem Ozevin, Hossein Ataei, Mehdi Modares, Asli Pelin Gurgun, Siamak Yazdani, Amarjit Singh
Publisher ISEC PRESS
Publication Year 2562
Keyword Snow height, Backpropagation neural network, Smoothing factor
Abstract 1. 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) are relying on a large number of input functions to estimate unknown or known occurrences. This paper attempts to apply 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
2. 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.
ISBN 978-0-9960437-6-2
Language English
URL Website https://www.isec-society.org/
Website title ISEC Society
International Struct Eng and Constructn Society

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