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.

Ground deformation observation is wildly concerned in the field of earthquake engineering. This paper proposes a high-precision displacement measurement technology based on both computer vision and numerical simulation. During the earthquake, the vision-based testing system collects visual data of the target installed on the location to be observed. The visual data streams can be quantified to the dynamic relative displacement value automatically, by employing mathematical vision algorithms and then by taking the relative displacement as an intermediate quantity, which is brought into the numerical model for iteration. When the test result is close to the simulated one, the absolute ground displacement data could be obtained approximately. A series of experiments have been carried out to suggest that the proposed method presents an innovative and low-cost solution to ground measurement in high accuracy. The method not only realizes the real-time ground deformation observation; moreover, it also provides a wider range of reliable data support to understand deformation mechanism, investigate seismic source information, and recognize the ground motion characteristics.