Multi-fidelity deep neural network with Monte Carlo dropout technique for uncertainty-aware risk recognition of backward erosion piping in dikes-Reference-Cited by-同舟云学术

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Multi-fidelity deep neural network with Monte Carlo dropout technique for uncertainty-aware risk recognition of backward erosion piping in dikes

Published:2024-11 Issue: Volume:166 Page:112165
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Liu Hongchen,Su Huaizhi,Yang Jiaquan,Li Haijiang

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference46 articles.

1. The statistics of embankment dam failures and accidents;Foster;Can. Geotech. J.,2000

2. Critical appraisal of piping phenomena in earth dams;Richards;Bull. Eng. Geol. Environ.,2007

3. Multiscale modeling of backward erosion piping in flood protection system infrastructure;Fascetti;Comput. -Aided Civ. Infrastruct. Eng.,2019

4. Measuring critical gradients for soil loosening and initiation of backward erosion-piping mechanism;Sige;J. Geotech. Geoenviron. Eng.,2020

5. Backward Erosion Piping;Beek,2013

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