Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam

Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam

Computational modeling plays a significant role in the design of rockfill dams. Various constitutive soil parameters are used to design such models, which often involve high uncertainties due to the complex structure of rockfill dams comprising various zones of different soil parameters. This study...

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Bibliographic Details
Main Authors: Gullnaz Shahzadi, Azzeddine Soulaïmani
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Water
Subjects:
sensitivity analysis
polynomial chaos expansion
uncertainty
deep neural networks
rockfill dams
Online Access:https://www.mdpi.com/2073-4441/13/13/1830

Internet

https://www.mdpi.com/2073-4441/13/13/1830

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