煤棚表面风压的多尺度长短期记忆神经网络预测方法

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

风荷载是影响大跨度空间结构安全性和稳定性的关键因素之一,因此研究结构表面风压对煤棚结构设计具有重要意义。风洞试验是获取煤棚结构表面风压的主要方法,但其存在成本较高和耗时较长的问题。利用风洞试验积累的大量数据发展风压快速预测方法是目前的研究热点之一。该文以长短期记忆(Long Short-Term Memory,LSTM)神经网络为基础,建立了煤棚结构风压时序预测模型,该模型利用高斯平滑将实验数据分为光滑数据和脉动数据,进而分别训练大尺度网络和小尺度网络。结果表明:所提出的多尺度网络预测模型可以实现对煤棚表面风压的快速预测,且相比于传统LSTM神经网络,多尺度LSTM神经网络具有误差小、精度高等优势。因此,基于LSTM的多尺度神经网络可以为煤棚等大跨空间结构表面风压提供依据。

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.