Data Driven Prediction of Seismic Ground Response under Low Level Excitation

It is an important task to model and predict seismic ground response; the results of ground response analysis are, in turn, used to assess liquefaction and integrity of undergound and upper structures. There has been numerious research and development on modelling of seismic ground response, but often there are quite large difference between prediction and measurement. In this study, it is attempted to train the input and output ground excitation data and make prediction based on it. To initiate this work, the deep learning network was trained for low level excitation data; the results showed reasonable match with actual measurements.

ACKNOWLEDGEMENT : The authors would like to thank the Ministry of Land, Infrastructure, and Transport of Korean government for the grant from Technology Advancement Research Program (grant no. 20CTAP-C152100-02) and Basic Science Research Program (grant no. 2017R1D1A3B03034563) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education.