Use of Artificial Neural Networks for Predicting Site Response from Ambient Noise HVSR-Reference-Cited by-同舟云学术

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Use of Artificial Neural Networks for Predicting Site Response from Ambient Noise HVSR

Published:2023-07-20 Issue: Volume: Page:
ISSN:
Container-title:Geo-Risk 2023
language:
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Author:

Anbazhagan Balakumar¹²³⁴⁵,Rodriguez-Marek Adrian¹²³⁴⁵,Zaker Esteghamati Mohsen¹²³⁴⁵,Kottke Albert¹²³⁴⁵,Abrahamson Norman¹²³⁴⁵

Affiliation:

1. Ph.D. Candidate, Charles E. Via. Dept. of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA.

2. Professor, Charles E. Via. Dept. of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA.

3. Assistant Professor, Dept. of Civil and Environmental Engineering, Utah State Univ., Logan, UT.

4. Geotechnical Earthquake Engineer, Pacific Gas and Electric Co., San Francisco, CA.

5. Adjunct Professor, Dept. of Civil and Environmental Engineering, UC Berkeley, Berkeley, CA.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784484975.020

Reference16 articles.

1. Summary of the ASK14 Ground Motion Relation for Active Crustal Regions

2. The Variability of Ground-Motion Prediction Models and Its Components

3. Bard, P. Y. 1995. Effects of surface geology on ground motion: recent results and remaining issues. In Proc. 10 European Conf. Earth. Eng., ed. Duma, Balkema, Rotterdam (pp. 305–323).

4. A systematic review on overfitting control in shallow and deep neural networks;Bejani M. M.;Artificial Intelligence Review,2021

5. Why Do Modern Probabilistic Seismic-Hazard Analyses Often Lead to Increased Hazard Estimates?

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