Machine learning-based prediction of site responses at liquefiable sites subjected to bi-directional ground motions-Reference-Cited by-同舟云学术

Machine learning-based prediction of site responses at liquefiable sites subjected to bi-directional ground motions

Published:2024 Issue:12 Volume:10 Page:311-316
ISSN:2188-8027
Container-title:Japanese Geotechnical Society Special Publication
language:en
Short-container-title:JGS Special Publication

Author:

Zhang Wenyang¹,Huang Yu-Wei²

Affiliation:

1. University of Texas at Austin

2. National Yang Ming Chiao Tung University

Publisher

The Japanese Geotechnical Society

Link

https://www.jstage.jst.go.jp/article/jgssp/10/12/10_v10.OS-1-02/_pdf

Reference29 articles.

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2. 2) Boulanger, R. W., & Ziotopoulou. (2015): PM4Sand (Version 3): A sand plasticity model for earthquake engineering applications. Center for Geotechnical Modeling Report No. UCD/CGM-15/01, Department of Civil and Environmental Engineering, University of California, Davis, Calif.

3. 3) Campbell, K. W., & Bozorgnia, Y. (2010): A ground motion prediction equation for the horizontal component of cumulative absolute velocity (CAV) based on the PEER-NGA strong motion database. Earthquake Spectra, 26(3), 635–650.

4. 4) Cetin, K. O., Seed, R. B., Kayen, R. E., Moss, R. E. S., Bilge, H. T., Ilgac, M., & Chowdhury, K. (2018): SPT-based probabilistic and deterministic assessment of seismic soil liquefaction triggering hazard. Soil Dynamics and Earthquake Engineering, 115, 698–709. https://doi.org/10.1016/j.soildyn.2018.09.012.

5. 5) Chen, G., Wang, Y., Zhao, D., Zhao, K., & Yang, J. (2021): A new effective stress method for nonlinear site response analyses. Earthquake Engineering & Structural Dynamics, 50(6), 1595–1611.