Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)

One of the purposes of earthquake engineering is to mitigate the damages in buildings and infrastructures and, therefore, reduce the impact of earthquakes on society. Seismic ground response analysis refers to the process of evaluating the ground surface motions based on the bedrock motion. On the other hand, deep learning techniques have been developing fast, and they are establishing their application in the civil engineering field. This study proposes two convolutional neural network (CNN) models to estimate the seismic response of the surface based on the seismic motion measured at 100 m level beneath the surface, and selected the one which outperforms the other as the main model. The performances of the main model are compared with those of a physical software SHAKE2000. Twelve sites that include 100 earthquake datasets, whose moment magnitude is higher than 6 and PGA is higher than 0.1 g were selected. In addition, the corresponding earthquake datasets are used for the CNN model. Whereas the conventional software overestimated the values of the amplitudes for most of the sites, the proposed CNN model predicts fairly well both the values of the amplitudes and the natural periods where responses amplify the most with few exceptions. The proposed model especially outperforms the conventional software when the natural periods range from 0.01 to 0.3 s. For specific sites, the average mean squared errors of the proposed model are even dozens of times lower than those of the physical conventional software.

Seismic Ground Response Prediction Based on Multilayer Perceptron

Earthquake disasters can cause enormous social and economic damage, and therefore the sustainability of infrastructure requires the mitigation of earthquake consequences. In seismic design of infrastructures, it is essential to estimate the response of the site during earthquake. Geotechnical engineers have developed quantitative methods for analyzing the seismic ground response. This study proposes a multilayer perceptron (MLP) model to evaluate the seismic response of the surface based on the seismic motion at the bedrock (or 100 m level), and compares its performance with that of a conventional model. A total of 6 sites, with 100 earthquake events at each site, were selected from the Kiban Kyoshin Network (KiK-net) and used as datasets. The acceleration response spectra were calculated from the predicted and measured (baseline) acceleration histories and compared. The proposed MLP model predicted the magnitudes of response and the natural periods where the response amplifies closely with the measured ground motions (baseline). The MLP model outperformed the conventional model for seismic ground response analysis. However, the proposed model did not perform as well for earthquakes whose response spectra exceed 2g due to a deficiency in large earthquake measurements in the training datasets.