MULTI-SCALE LONG-SHORT TERM MEMORY NEURAL NETWORK METHOD FOR PREDICTING WIND PRESSURE ON COAL SHED SURFACE

Wind loading is a crucial factor affecting the safety and stability of long-span space structures. Therefore, studying the surface wind pressure is important for the design of coal shed structures. The wind tunnel test is the main method for obtaining the surface wind pressure of coal shed structure. However, it is associated with high costs and time-consuming procedures. One of the current research hotspots to develop a rapid wind pressure prediction method is adopting the large amount of data accumulated from wind tunnel test. In this study, a time series prediction model for coal shed structure wind pressure is thusly established upon Long Short-Term Memory (LSTM) neural network. The model utilizes Gaussian smoothing to divide experimental data into smooth and pulsating data, and then trains large-scale and small-scale networks separately. The results show that the multi-scale network prediction model proposed can achieve the rapid wind pressure prediction on the surface of coal shed. Compared to traditional LSTM neural network, the multi-scale LSTM neural network demonstrates advantages such as lower error and higher accuracy. Therefore, the multi-scale network based on LSTM can provide a basis for predicting surface wind pressure for long-span spatial structures such as coal sheds.