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Prediction of Cumulative Absolute Velocity Based on Refined Second-order Deep Neural Network.
- Source :
-
Journal of Earthquake Engineering . Nov2022, Vol. 26 Issue 15, p8021-8040. 20p. - Publication Year :
- 2022
-
Abstract
- This study aims to develop a reliable ground motion model (GMM) for CAV by using ground motion (GM) recordings from the PEER NGA-West2 database. A total of 17,684 GM recordings are chosen and randomly separated into the training, validation, and testing datasets. The DNN is advanced by incorporating the refined second-order (RSO) neuron. The effect of seismological and site-specific parameters on the predicted CAV is investigated. The comparative assessment of four existing models with the RSO-DNN model of this study highlights the superior prediction skill of the latter one since the RSO-DNN model is found to be associated with considerably less error. [ABSTRACT FROM AUTHOR]
- Subjects :
- *GROUND motion
*VELOCITY
Subjects
Details
- Language :
- English
- ISSN :
- 13632469
- Volume :
- 26
- Issue :
- 15
- Database :
- Academic Search Index
- Journal :
- Journal of Earthquake Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 159933820
- Full Text :
- https://doi.org/10.1080/13632469.2021.1985017