English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse
  1. Search Results / 
  2. Item Summary

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary
 PreviousNext  

Released
Conference Paper

Use of machine learning to predict seismic site amplification of shallow bedrock sites

Authors

Lee,  Yong-Guk
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Jae-Kwang,  Ahn
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Park,  Duhee
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

External Ressource
No external resources are shared
Fulltext (public)
There are no public fulltexts stored in GFZpublic
Supplementary Material (public)
There is no public supplementary material available
Citation

Lee, Y.-G., Jae-Kwang, A., Park, D. (2023): Use of machine learning to predict seismic site amplification of shallow bedrock sites, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-3723


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5020820
Abstract
We use a number of machine learning models to predict the seismic site amplifications of shallow bedrock sites calculated from one-dimensional ground response analyses. We compare the results with a regression-based model that are conditioned on VS30, site period, and peak ground acceleration. We show that use of the array type of information for the ground motion and site profile in machine learning training produces excellent predictions of the surface response. In contrast, employing parameters typically used for the site amplification models widely employed in ground motion prediction equations produces only marginal improvement compared with a regression-based model. The fit can be improved through utilization of additional parameters, although the improvement is not as dramatic as when using the array data.