EGU2020-12721, updated on 12 Jun 2020
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Data Driven Prediction of Seismic Ground Response under Low Level Excitation

Jaewon Yoo1 and Jaehun Ahn2
Jaewon Yoo and Jaehun Ahn
  • 1Pusan National University, Research Institute of Industrial Technology, Geotechnical Engineering, Korea, Republic of (
  • 2Pusan National University, Civil Engineering, Korea, Republic of (

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.

How to cite: Yoo, J. and Ahn, J.: Data Driven Prediction of Seismic Ground Response under Low Level Excitation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12721,, 2020

This abstract will not be presented.