Estimation of ground motion parameters via multi-task deep neural networks-Reference-Cited by-同舟云学术

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Estimation of ground motion parameters via multi-task deep neural networks

Published:2024-03-04 Issue:7 Volume:120 Page:6737-6754
ISSN:0921-030X
Container-title:Natural Hazards
language:en
Short-container-title:Nat Hazards

Author:

Meng Fanchun,Ren Tao^ORCID,Guo Enming,Chen Hongfeng,Liu Xinliang,Zhang Haodong,Li Jiang

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Joint Fund of Science & Technology Department of Liaoning Province and State Key Laboratory of Robotics, china

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11069-024-06464-w.pdf

Reference33 articles.

1. Alavi AH, Gandomi AH (2011) Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing. Comput Struct 89(23–24):2176–2194. https://doi.org/10.1016/j.compstruc.2011.08.019

2. Alavi AH, Gandomi AH, Modaresnezhad M, Mousavi M (2011) New ground-motion prediction equations using multi expression programing. J Earthq Eng 15(4):511–536. https://doi.org/10.1080/13632469.2010.526752

3. Derakhshani A, Foruzan AH (2019) Predicting the principal strong ground motion parameters: a deep learning approach. Appl Soft Comput 80:192–201. https://doi.org/10.1016/j.asoc.2019.03.029

4. Gandomi AH, Alavi AH, Mousavi M, Tabatabaei SM (2011) A hybrid computational approach to derive new ground-motion prediction equations. Eng Appl Artif Intell 24(4):717–732. https://doi.org/10.1016/j.engappai.2011.01.005

5. Güllü H, Erçelebi E (2007) A neural network approach for attenuation relationships: an application using strong ground motion data from Turkey. Eng Geol 93(3–4):65–81. https://doi.org/10.1016/j.enggeo.2007.05.004

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