Real-Time Prediction of the Trend of Ground Motion Intensity Based on Deep Learning - Tao Liu; Zhijun Dai (Shock and Vibration)
In order to predict the intensity of earthquake damage in advance and improve the effectiveness of earthquake emergency measures, this paper proposes a deep learning model for real-time prediction of the trend of ground motion intensity. The input sample is the real-time monitoring recordings of the current received ground motion acceleration. According to the different sampling frequencies, the neural network is constructed by several subnetworks, and the output of each subnetwork is combined into one. After the training and verification of the model, the results show that the model has an accuracy rate of 75% on the testing set, which is effective on real-time prediction of the ground motion intensity. Moreover, the correlation between the Arias intensity and structural damage is stronger than the correlation between peak acceleration and structural damage, so the model is useful for determining real-time response measures on earthquake disaster prevention and mitigation compared with the current more common antiseismic measures based on predictive PGA.